
#boston data set train test
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score
from sklearn.model_selection import train_test_split
import os
os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Datasets")
boston=pd.read_csv('Boston.csv')

#divide the set into 70 to 30 percentage ratio
bs_train,bs_test=train_test_split(boston,test_size=0.3,random_state=23)

#train the set of x and y
x_train=bs_train[['crim','lstat']].values
y_train=bs_train['medv'].values

#print(x_train.shape,y_train.shape)
#print(x_test.shape,y_test.shape)

#test the model in remaining 30%
x_test=bs_test[['crim','lstat']].values
y_test=bs_test['medv'].values

#apply the linear regression model by calling lr function 1st fit the dataset
lr=LinearRegression()

lr.fit(x_train,y_train)   #fit the dataset

y_pred=lr.predict(x_test)


print('coeff of determination :',r2_score(y_test,y_pred))

coeff of determination : 0.5046158277013255


#heat maps for correlation
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt


pizza = pd.read_csv("pizza.csv")


plt.figure(figsize=(12, 10))


heatmap = sns.heatmap(data=pizza.corr(), cmap='plasma', annot=True, linewidths=1, square=True,
                      annot_kws={"size": 20}, cbar_kws={"shrink": 0.8})
plt.title("Heatmap of pizza Dataset Correlation",color='r',fontsize=35)


plt.show()


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt



insure = pd.read_csv("Insure_auto.csv")


plt.figure(figsize=(12, 10))


heatmap = sns.heatmap(data=insure.corr(), cmap='inferno', annot=True, linewidths=1, square=True,
                      annot_kws={"size": 20}, cbar_kws={"shrink": 0.8})
plt.title("Heatmap of Insure auto Dataset Correlation",color='violet',fontsize=35)



plt.show()


import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

np.random.seed(42)
X1 = np.random.rand(100)
X2 = np.random.rand(100)

noise = np.random.normal(0, 0.1, 100)


y = 2 * X1 + 3 * X2 + noise

# Combine features into a single array
X = np.column_stack((X1, X2))

# Visualize the points in 2D
plt.scatter(X1, y, label='x1 vs y')
plt.scatter(X2, y, label='x2 vs y')
plt.xlabel('X1 and X2')
plt.ylabel('y')
plt.legend()
plt.title('2D Visualization')
plt.show()

# Visualize the points in 3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X1, X2, y,color='r')
ax.set_xlabel('X1')
ax.set_ylabel('X2')
ax.set_zlabel('y')
ax.set_title('3D Visualization')
plt.show()

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)


model = LinearRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)

# Evaluate the performance of the model
mse = mean_squared_error(y_test, y_pred)

print(f'Mean Squared Error: {mse}')

# Visualize the predicted vs actual values in 2D
plt.scatter(y_test, y_pred)
plt.xlabel('Actual y')
plt.ylabel('Predicted y')
plt.title('Actual vs Predicted Values')
plt.show()

Mean Squared Error: 0.00837269228376218


e=pd.read_csv('electricityConsumptionAndProductioction.csv')
e


import pandas as pd

e= 'electricityConsumptionAndProductioction.csv'


e = pd.read_csv(e)


summary_statistics = e.describe()


q1 = e.quantile(0.25)
q2 = e.quantile(0.5)  # Equivalent to median
q3 = e.quantile(0.75)

print("Q1 (25th percentile):\n", q1)
print("\nQ2 (50th percentile - Median):\n", q2)
print("\nQ3 (75th percentile):\n", q3)

Q1 (25th percentile):
 Consumption      5895.75
Production       5833.00
Nuclear          1349.00
Wind              226.00
Hydroelectric    1310.00
Oil and Gas       881.00
Coal             1080.00
Solar               0.00
Biomass            49.00
Name: 0.25, dtype: float64

Q2 (50th percentile - Median):
 Consumption      6686.0
Production       6492.0
Nuclear          1386.0
Wind              577.0
Hydroelectric    1708.0
Oil and Gas      1216.0
Coal             1254.0
Solar               2.0
Biomass            61.0
Name: 0.5, dtype: float64

Q3 (75th percentile):
 Consumption      7434.00
Production       7210.00
Nuclear          1409.00
Wind             1195.25
Hydroelectric    2184.00
Oil and Gas      1502.00
Coal             1467.00
Solar             268.00
Biomass            68.00
Name: 0.75, dtype: float64


e = 'electricityConsumptionAndProductioction.csv'

pd.read_csv(e)


import pandas as pd

el = 'electricityConsumptionAndProductioction.csv'
e = pd.read_csv(file_path)
col = 'Production'
#avg nuclear production
avg = e[col].mean()

print(f"The average nuclear facility production is: {avg}")

The average nuclear facility production is: 6543.770477537257


e.count([e.isnull()])

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_12488/922990842.py in <module>
----> 1 e.count([e.isnull()])

~\anaconda3\lib\site-packages\pandas\core\frame.py in count(self, axis, level, numeric_only)
   9693         dtype: int64
   9694         """
-> 9695         axis = self._get_axis_number(axis)
   9696         if level is not None:
   9697             warnings.warn(

~\anaconda3\lib\site-packages\pandas\core\generic.py in _get_axis_number(cls, axis)
    544     def _get_axis_number(cls, axis: Axis) -> int:
    545         try:
--> 546             return cls._AXIS_TO_AXIS_NUMBER[axis]
    547         except KeyError:
    548             raise ValueError(f"No axis named {axis} for object type {cls.__name__}")

TypeError: unhashable type: 'list'


null_values = e.isnull().sum()
null_values

DateTime         0
Consumption      0
Production       0
Nuclear          0
Wind             0
Hydroelectric    0
Oil and Gas      0
Coal             0
Solar            0
Biomass          0
dtype: int64


import matplotlib.pyplot as plt
el= 'electricityConsumptionAndProductioction.csv'


e = pd.read_csv(el)
plt.bar(e['Consumption'], label='Consumption', color='blue')
plt.bar(e['Production'], label='Production', color='green')

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
Cell In[6], line 5
      1 import matplotlib.pyplot as plt
      2 el= 'electricityConsumptionAndProductioction.csv'
----> 5 e = pd.read_csv(el)
      6 plt.bar(e['Consumption'], label='Consumption', color='blue')
      7 plt.bar(e['Production'], label='Production', color='green')

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:912, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
    899 kwds_defaults = _refine_defaults_read(
    900     dialect,
    901     delimiter,
   (...)
    908     dtype_backend=dtype_backend,
    909 )
    910 kwds.update(kwds_defaults)
--> 912 return _read(filepath_or_buffer, kwds)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:577, in _read(filepath_or_buffer, kwds)
    574 _validate_names(kwds.get("names", None))
    576 # Create the parser.
--> 577 parser = TextFileReader(filepath_or_buffer, **kwds)
    579 if chunksize or iterator:
    580     return parser

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1407, in TextFileReader.__init__(self, f, engine, **kwds)
   1404     self.options["has_index_names"] = kwds["has_index_names"]
   1406 self.handles: IOHandles | None = None
-> 1407 self._engine = self._make_engine(f, self.engine)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1661, in TextFileReader._make_engine(self, f, engine)
   1659     if "b" not in mode:
   1660         mode += "b"
-> 1661 self.handles = get_handle(
   1662     f,
   1663     mode,
   1664     encoding=self.options.get("encoding", None),
   1665     compression=self.options.get("compression", None),
   1666     memory_map=self.options.get("memory_map", False),
   1667     is_text=is_text,
   1668     errors=self.options.get("encoding_errors", "strict"),
   1669     storage_options=self.options.get("storage_options", None),
   1670 )
   1671 assert self.handles is not None
   1672 f = self.handles.handle

File ~\anaconda3\Lib\site-packages\pandas\io\common.py:859, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    854 elif isinstance(handle, str):
    855     # Check whether the filename is to be opened in binary mode.
    856     # Binary mode does not support 'encoding' and 'newline'.
    857     if ioargs.encoding and "b" not in ioargs.mode:
    858         # Encoding
--> 859         handle = open(
    860             handle,
    861             ioargs.mode,
    862             encoding=ioargs.encoding,
    863             errors=errors,
    864             newline="",
    865         )
    866     else:
    867         # Binary mode
    868         handle = open(handle, ioargs.mode)

FileNotFoundError: [Errno 2] No such file or directory: 'electricityConsumptionAndProductioction.csv'


el= 'electricityConsumptionAndProductioction.csv'


e = pd.read_csv(el)


e['year'] = pd.DatetimeIndex(e['DateTime']).year
e=e.drop('Year',axis=1)
e


import pandas as pd
import matplotlib.pyplot as plt
import os
os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Datasets")

el = 'electricityConsumptionAndProduction.csv'  # Corrected the file name

e = pd.read_csv(el)
e['year'] = pd.DatetimeIndex(e['DateTime']).year

yr = 'year'
production = 'Production'
demand = 'Consumption'

y = e[e[production] > e[demand]][yr]
yt = y.tolist()

if not y.empty:
    print("Years where production was more than demand:", list(set(yt)))
    plt.plot(e[yr], e[production], label='Production', marker='o')
    plt.plot(e[yr], e[demand], label='Demand', marker='o')
    plt.xlabel('Year')
    plt.ylabel('Amount')
    plt.title('Production and Demand Over Years')
    plt.legend()
    plt.show()
else:
    print("Production was not more than demand in any year.")

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
Cell In[10], line 8
      4 os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Datasets")
      6 el = 'electricityConsumptionAndProduction.csv'  # Corrected the file name
----> 8 e = pd.read_csv(el)
      9 e['year'] = pd.DatetimeIndex(e['DateTime']).year
     11 yr = 'year'

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:912, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
    899 kwds_defaults = _refine_defaults_read(
    900     dialect,
    901     delimiter,
   (...)
    908     dtype_backend=dtype_backend,
    909 )
    910 kwds.update(kwds_defaults)
--> 912 return _read(filepath_or_buffer, kwds)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:577, in _read(filepath_or_buffer, kwds)
    574 _validate_names(kwds.get("names", None))
    576 # Create the parser.
--> 577 parser = TextFileReader(filepath_or_buffer, **kwds)
    579 if chunksize or iterator:
    580     return parser

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1407, in TextFileReader.__init__(self, f, engine, **kwds)
   1404     self.options["has_index_names"] = kwds["has_index_names"]
   1406 self.handles: IOHandles | None = None
-> 1407 self._engine = self._make_engine(f, self.engine)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1661, in TextFileReader._make_engine(self, f, engine)
   1659     if "b" not in mode:
   1660         mode += "b"
-> 1661 self.handles = get_handle(
   1662     f,
   1663     mode,
   1664     encoding=self.options.get("encoding", None),
   1665     compression=self.options.get("compression", None),
   1666     memory_map=self.options.get("memory_map", False),
   1667     is_text=is_text,
   1668     errors=self.options.get("encoding_errors", "strict"),
   1669     storage_options=self.options.get("storage_options", None),
   1670 )
   1671 assert self.handles is not None
   1672 f = self.handles.handle

File ~\anaconda3\Lib\site-packages\pandas\io\common.py:859, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    854 elif isinstance(handle, str):
    855     # Check whether the filename is to be opened in binary mode.
    856     # Binary mode does not support 'encoding' and 'newline'.
    857     if ioargs.encoding and "b" not in ioargs.mode:
    858         # Encoding
--> 859         handle = open(
    860             handle,
    861             ioargs.mode,
    862             encoding=ioargs.encoding,
    863             errors=errors,
    864             newline="",
    865         )
    866     else:
    867         # Binary mode
    868         handle = open(handle, ioargs.mode)

FileNotFoundError: [Errno 2] No such file or directory: 'electricityConsumptionAndProduction.csv'


import pandas as pd
import matplotlib.pyplot as plt

el = 'electricityConsumptionAndProduction.csv'  # Corrected the file name

e = pd.read_csv(el)
e['year'] = pd.DatetimeIndex(e['DateTime']).year

yr = 'year'
production = 'Production'
demand = 'Consumption'

# Grouping the dataset into 3 categories based on the condition
grouped_data = e.groupby((e[production] > e[demand]).cumsum())

# Plotting each group separately
for name, group in grouped_data:
    plt.plot(group[yr], group[production], label=f'Group {name}', marker='o')

plt.xlabel('Year')
plt.ylabel('Amount')
plt.title('Years with Production > Demand')
plt.legend()
plt.show()

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
Cell In[5], line 6
      2 import matplotlib.pyplot as plt
      4 el = 'electricityConsumptionAndProduction.csv'  # Corrected the file name
----> 6 e = pd.read_csv(el)
      7 e['year'] = pd.DatetimeIndex(e['DateTime']).year
      9 yr = 'year'

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:912, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
    899 kwds_defaults = _refine_defaults_read(
    900     dialect,
    901     delimiter,
   (...)
    908     dtype_backend=dtype_backend,
    909 )
    910 kwds.update(kwds_defaults)
--> 912 return _read(filepath_or_buffer, kwds)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:577, in _read(filepath_or_buffer, kwds)
    574 _validate_names(kwds.get("names", None))
    576 # Create the parser.
--> 577 parser = TextFileReader(filepath_or_buffer, **kwds)
    579 if chunksize or iterator:
    580     return parser

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1407, in TextFileReader.__init__(self, f, engine, **kwds)
   1404     self.options["has_index_names"] = kwds["has_index_names"]
   1406 self.handles: IOHandles | None = None
-> 1407 self._engine = self._make_engine(f, self.engine)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1661, in TextFileReader._make_engine(self, f, engine)
   1659     if "b" not in mode:
   1660         mode += "b"
-> 1661 self.handles = get_handle(
   1662     f,
   1663     mode,
   1664     encoding=self.options.get("encoding", None),
   1665     compression=self.options.get("compression", None),
   1666     memory_map=self.options.get("memory_map", False),
   1667     is_text=is_text,
   1668     errors=self.options.get("encoding_errors", "strict"),
   1669     storage_options=self.options.get("storage_options", None),
   1670 )
   1671 assert self.handles is not None
   1672 f = self.handles.handle

File ~\anaconda3\Lib\site-packages\pandas\io\common.py:859, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    854 elif isinstance(handle, str):
    855     # Check whether the filename is to be opened in binary mode.
    856     # Binary mode does not support 'encoding' and 'newline'.
    857     if ioargs.encoding and "b" not in ioargs.mode:
    858         # Encoding
--> 859         handle = open(
    860             handle,
    861             ioargs.mode,
    862             encoding=ioargs.encoding,
    863             errors=errors,
    864             newline="",
    865         )
    866     else:
    867         # Binary mode
    868         handle = open(handle, ioargs.mode)

FileNotFoundError: [Errno 2] No such file or directory: 'electricityConsumptionAndProduction.csv'


# prompt: correct this program it need to time series chart but this is wrong 

import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime

df = pd.read_csv('electricityConsumptionAndProductioction.csv')
df['Date'] = pd.to_datetime(df['Date'])

plt.plot(df['Date'], df['Value'])
plt.xlabel('Date')
plt.ylabel('Value')
plt.title('Time Series Chart')
plt.show()

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
Cell In[4], line 7
      4 import matplotlib.pyplot as plt
      5 from datetime import datetime
----> 7 df = pd.read_csv('electricityConsumptionAndProductioction.csv')
      8 df['Date'] = pd.to_datetime(df['Date'])
     10 plt.plot(df['Date'], df['Value'])

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:912, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
    899 kwds_defaults = _refine_defaults_read(
    900     dialect,
    901     delimiter,
   (...)
    908     dtype_backend=dtype_backend,
    909 )
    910 kwds.update(kwds_defaults)
--> 912 return _read(filepath_or_buffer, kwds)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:577, in _read(filepath_or_buffer, kwds)
    574 _validate_names(kwds.get("names", None))
    576 # Create the parser.
--> 577 parser = TextFileReader(filepath_or_buffer, **kwds)
    579 if chunksize or iterator:
    580     return parser

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1407, in TextFileReader.__init__(self, f, engine, **kwds)
   1404     self.options["has_index_names"] = kwds["has_index_names"]
   1406 self.handles: IOHandles | None = None
-> 1407 self._engine = self._make_engine(f, self.engine)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1661, in TextFileReader._make_engine(self, f, engine)
   1659     if "b" not in mode:
   1660         mode += "b"
-> 1661 self.handles = get_handle(
   1662     f,
   1663     mode,
   1664     encoding=self.options.get("encoding", None),
   1665     compression=self.options.get("compression", None),
   1666     memory_map=self.options.get("memory_map", False),
   1667     is_text=is_text,
   1668     errors=self.options.get("encoding_errors", "strict"),
   1669     storage_options=self.options.get("storage_options", None),
   1670 )
   1671 assert self.handles is not None
   1672 f = self.handles.handle

File ~\anaconda3\Lib\site-packages\pandas\io\common.py:859, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    854 elif isinstance(handle, str):
    855     # Check whether the filename is to be opened in binary mode.
    856     # Binary mode does not support 'encoding' and 'newline'.
    857     if ioargs.encoding and "b" not in ioargs.mode:
    858         # Encoding
--> 859         handle = open(
    860             handle,
    861             ioargs.mode,
    862             encoding=ioargs.encoding,
    863             errors=errors,
    864             newline="",
    865         )
    866     else:
    867         # Binary mode
    868         handle = open(handle, ioargs.mode)

FileNotFoundError: [Errno 2] No such file or directory: 'electricityConsumptionAndProductioction.csv'


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import ElasticNet
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import os

# prompt: in the above, I have alphas=[0.001, 0.1, 0.5, 1, 2] and l1_s=[0.001, 0.25, 0.75].
# Using these l1_s, which is l1_ratio, do the above problem.

# boston data set train test
os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Datasets")
boston = pd.read_csv('Boston.csv')

# divide the set into a 70 to 30 percentage ratio
bs_train, bs_test = train_test_split(boston, test_size=0.3, random_state=23)

# train the set of x and y
x_train = bs_train.drop('medv', axis=1).values
y_train = bs_train['medv'].values

# test the model on the remaining 30%
x_test = bs_test.drop('medv', axis=1).values
y_test = bs_test['medv'].values

l1_s = [0.001, 0.25, 0.75]
alphas = [0.001, 0.1, 0.5, 1, 2]
scores = []

best_score = r2_score(y_test, ElasticNet(alpha=alphas[0], l1_ratio=l1_s[0]).fit(x_train, y_train).predict(x_test))

for v in l1_s:
    for w in alphas:
        elastic_net = ElasticNet(alpha=w, l1_ratio=v)
        elastic_net.fit(x_train, y_train)
        y_pred = elastic_net.predict(x_test)
        scr = r2_score(y_test, y_pred)
        scores.append(scr)

        if scr < best_score: 
            print("Stopping the loop as R^2 score is lesser than the previous best score.")
            break
        else:
            best_score = scr

    if scr < best_score:
        break  
i_max = np.argmax(scores)
print("best alpha =", alphas[i_max % 5])
print("Best score =", scores[i_max])
print("Best l1_ratio =", l1_s[i_max // 5])

print('coeff of determination:', r2_score(y_test, y_pred))

Stopping the loop as R^2 score is lesser than the previous best score.
best alpha = 0.001
Best score = 0.6935414909676965
Best l1_ratio = 0.001
coeff of determination: 0.6925494448185865


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import ElasticNet
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import os


pd.read_csv('CarPrice_Assignment.csv')
car_names = car_data['CarName']

unique_car_companies = car_names.apply(lambda x: x.split(' ')[0])

num_unique_car_companies = unique_car_companies.nunique()

print(f"Number of unique car companies: {num_unique_car_companies}")

Number of unique car companies: 3


import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

car_data = pd.read_csv('CarPrice_Assignment.csv')


car_data['CarCompany'] = car_data['CarName'].apply(lambda x: x.split(' ')[0])

plt.figure(figsize=(15, 8))
sns.boxplot(x='CarCompany', y='price', data=car_data)
plt.title('Car Company vs Price')
plt.xticks(rotation=90)
plt.show()

plt.figure(figsize=(8, 6))
sns.boxplot(y='price', data=car_data)
plt.title('Price Distribution with Outliers')
plt.show()


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
car_data = pd.read_csv('CarPrice_Assignment.csv')

plt.figure(figsize=(8, 6))
sns.countplot(x='fueltype', data=car_data)
plt.title('Distribution of Fuel Type')
plt.show()

plt.figure(figsize=(8, 6))
sns.countplot(x='aspiration', data=car_data)
plt.title('Distribution of Aspiration')
plt.show()

plt.figure(figsize=(10, 6))
sns.countplot(x='carbody', data=car_data)
plt.title('Distribution of Car Body Style')
plt.show()


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, cross_val_score, KFold, GridSearchCV
from sklearn.metrics import accuracy_score
import os

os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Bankruptcy")

# Load the dataset
bk = pd.read_csv('Bankruptcy.csv')
dum_bk = pd.get_dummies(bk, drop_first=True)

print(bk['D'].value_counts())
print(bk['D'].value_counts(normalize=True) * 100)

train, test = train_test_split(dum_bk, test_size=0.3, stratify=bk['D'], random_state=23)

x_train = train.drop('D', axis=1)
y_train = train['D']

x_test = test.drop('D', axis=1)
y_test = test['D']

print(y_train.value_counts(normalize=True) * 100)
print(y_test.value_counts(normalize=True) * 100)

lr = LogisticRegression()

# Fit the model
lr.fit(x_train, y_train)
print("Coefficients:", lr.coef_)
print("Intercept:", lr.intercept_)

# Predictions
y_pred = lr.predict(x_test)
y_predict_prob = lr.predict_proba(x_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

# GridSearchCV for Logistic Regression
penalties = ['l1', 'l2','elasticnet',None]
l1_ratios = np.linspace(0, 1, 5)
params_lr = {'penalty': penalties, 'l1_ratio': l1_ratios}

gcv_lr = GridSearchCV(lr, param_grid=params_lr, cv=5)
gcv_lr.fit(x_train, y_train)

print("Logistic Regression Best Params:", gcv_lr.best_params_)
print("Logistic Regression Best Score:", gcv_lr.best_score_)

D
0    66
1    66
Name: count, dtype: int64
D
0    50.0
1    50.0
Name: proportion, dtype: float64
D
1    50.0
0    50.0
Name: proportion, dtype: float64
D
0    50.0
1    50.0
Name: proportion, dtype: float64
Coefficients: [[ 0.77644127 -0.12602391  0.24668336  0.04282741  0.01796036  0.04872628
   0.0218713   0.01250506  0.03807607 -0.03904191  0.35850119  0.01136452
  -0.01089411 -0.03746507  0.01869797  0.01462473  0.02996913  0.02985431
   0.02325577  0.05313726 -0.0345866  -0.03743207  0.12532848  0.02643464
   0.02084258  0.04890375]]
Intercept: [-42.54498452]
Accuracy: 1.0

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(

Logistic Regression Best Params: {'l1_ratio': 0.0, 'penalty': 'l2'}
Logistic Regression Best Score: 0.9888888888888889

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:425: FitFailedWarning: 
50 fits failed out of a total of 100.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
25 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1168, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
25 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1168, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  warnings.warn(some_fits_failed_message, FitFailedWarning)
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:976: UserWarning: One or more of the test scores are non-finite: [       nan 0.98888889        nan 0.98888889        nan 0.98888889
        nan 0.98888889        nan 0.98888889        nan 0.98888889
        nan 0.98888889        nan 0.98888889        nan 0.98888889
        nan 0.98888889]
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, cross_val_score, KFold, GridSearchCV
from sklearn.metrics import accuracy_score
import os

os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Bankruptcy")

# Load the dataset
bk = pd.read_csv('Bankruptcy.csv')
dum_bk = pd.get_dummies(bk, drop_first=True)

print(bk['D'].value_counts())
print(bk['D'].value_counts(normalize=True) * 100)

train, test = train_test_split(dum_bk, test_size=0.3, stratify=bk['D'], random_state=23)

x_train = train.drop('D', axis=1)
y_train = train['D']

x_test = test.drop('D', axis=1)
y_test = test['D']

print(y_train.value_counts(normalize=True) * 100)
print(y_test.value_counts(normalize=True) * 100)

lr = LogisticRegression()

# Fit the model
lr.fit(x_train, y_train)
print("Coefficients:", lr.coef_)
print("Intercept:", lr.intercept_)

# Predictions
y_pred = lr.predict(x_test)
y_predict_prob = lr.predict_proba(x_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

# GridSearchCV for Logistic Regression
penalties = ['l1', 'l2','elasticnet',None]
l1_ratios = np.linspace(0, 1, 5)
params_lr = {'penalty': penalties, 'l1_ratio': l1_ratios}

gcv_lr = GridSearchCV(lr, param_grid=params_lr, cv=5)
gcv_lr.fit(x_train, y_train)

print("Logistic Regression Best Params:", gcv_lr.best_params_)
print("Logistic Regression Best Score:", gcv_lr.best_score_)

D
0    66
1    66
Name: count, dtype: int64
D
0    50.0
1    50.0
Name: proportion, dtype: float64
D
1    50.0
0    50.0
Name: proportion, dtype: float64
D
0    50.0
1    50.0
Name: proportion, dtype: float64
Coefficients: [[ 0.77644127 -0.12602391  0.24668336  0.04282741  0.01796036  0.04872628
   0.0218713   0.01250506  0.03807607 -0.03904191  0.35850119  0.01136452
  -0.01089411 -0.03746507  0.01869797  0.01462473  0.02996913  0.02985431
   0.02325577  0.05313726 -0.0345866  -0.03743207  0.12532848  0.02643464
   0.02084258  0.04890375]]
Intercept: [-42.54498452]
Accuracy: 1.0

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(

Logistic Regression Best Params: {'l1_ratio': 0.0, 'penalty': 'l2'}
Logistic Regression Best Score: 0.9888888888888889

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=None)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:425: FitFailedWarning: 
50 fits failed out of a total of 100.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
25 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1168, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
25 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1168, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  warnings.warn(some_fits_failed_message, FitFailedWarning)
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:976: UserWarning: One or more of the test scores are non-finite: [       nan 0.98888889        nan 0.98888889        nan 0.98888889
        nan 0.98888889        nan 0.98888889        nan 0.98888889
        nan 0.98888889        nan 0.98888889        nan 0.98888889
        nan 0.98888889]
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1171: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


#################### Bankruptcy ############################
import os
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, StratifiedKFold

# Change the directory
os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Titanic")

# Load the data
t = pd.read_csv("train_data.csv", index_col=0)

# Separate features and target variable
X = t.drop(['Fare'], axis=1)
y = t['Fare']

# Create a Logistic Regression model
lr = LogisticRegression()

# Create a StratifiedKFold object
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=23)

# Define the parameter grid for GridSearchCV
params = {
    'penalty': ['l1', 'l2', 'elasticnet', 'none'],
    'l1_ratio': np.linspace(0, 1, 5),
    'solver': ['lbfgs', 'liblinear', 'newton-cg', 'sag', 'saga']
}

# Create GridSearchCV object
gcv = GridSearchCV(lr, param_grid=params, cv=kfold)
gcv.fit(X, y)

# Print the best parameters and score
print("Best Parameters:", gcv.best_params_)
print("Best Score:", gcv.best_score_)

# Create a DataFrame for the GridSearchCV results
pd_cv = pd.DataFrame(gcv.cv_results_)

# Fit the model with the best parameters on the whole dataset
best_model = LogisticRegression(**gcv.best_params_)
best_model.fit(X, y)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[4], line 33
     31 # Create GridSearchCV object
     32 gcv = GridSearchCV(lr, param_grid=params, cv=kfold)
---> 33 gcv.fit(X, y)
     35 # Print the best parameters and score
     36 print("Best Parameters:", gcv.best_params_)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1151, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1144     estimator._validate_params()
   1146 with config_context(
   1147     skip_parameter_validation=(
   1148         prefer_skip_nested_validation or global_skip_validation
   1149     )
   1150 ):
-> 1151     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1419, in GridSearchCV._run_search(self, evaluate_candidates)
   1417 def _run_search(self, evaluate_candidates):
   1418     """Search all candidates in param_grid"""
-> 1419     evaluate_candidates(ParameterGrid(self.param_grid))

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:857, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    837 if self.verbose > 0:
    838     print(
    839         "Fitting {0} folds for each of {1} candidates,"
    840         " totalling {2} fits".format(
    841             n_splits, n_candidates, n_candidates * n_splits
    842         )
    843     )
    845 out = parallel(
    846     delayed(_fit_and_score)(
    847         clone(base_estimator),
    848         X,
    849         y,
    850         train=train,
    851         test=test,
    852         parameters=parameters,
    853         split_progress=(split_idx, n_splits),
    854         candidate_progress=(cand_idx, n_candidates),
    855         **fit_and_score_kwargs,
    856     )
--> 857     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    858         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    859     )
    860 )
    862 if len(out) < 1:
    863     raise ValueError(
    864         "No fits were performed. "
    865         "Was the CV iterator empty? "
    866         "Were there no candidates?"
    867     )

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_split.py:377, in _BaseKFold.split(self, X, y, groups)
    369 if self.n_splits > n_samples:
    370     raise ValueError(
    371         (
    372             "Cannot have number of splits n_splits={0} greater"
    373             " than the number of samples: n_samples={1}."
    374         ).format(self.n_splits, n_samples)
    375     )
--> 377 for train, test in super().split(X, y, groups):
    378     yield train, test

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_split.py:108, in BaseCrossValidator.split(self, X, y, groups)
    106 X, y, groups = indexable(X, y, groups)
    107 indices = np.arange(_num_samples(X))
--> 108 for test_index in self._iter_test_masks(X, y, groups):
    109     train_index = indices[np.logical_not(test_index)]
    110     test_index = indices[test_index]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_split.py:758, in StratifiedKFold._iter_test_masks(self, X, y, groups)
    757 def _iter_test_masks(self, X, y=None, groups=None):
--> 758     test_folds = self._make_test_folds(X, y)
    759     for i in range(self.n_splits):
    760         yield test_folds == i

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_split.py:701, in StratifiedKFold._make_test_folds(self, X, y)
    699 allowed_target_types = ("binary", "multiclass")
    700 if type_of_target_y not in allowed_target_types:
--> 701     raise ValueError(
    702         "Supported target types are: {}. Got {!r} instead.".format(
    703             allowed_target_types, type_of_target_y
    704         )
    705     )
    707 y = column_or_1d(y)
    709 _, y_idx, y_inv = np.unique(y, return_index=True, return_inverse=True)

ValueError: Supported target types are: ('binary', 'multiclass'). Got 'continuous' instead.


import os
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, StratifiedKFold

# Change the directory
os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Titanic")

# Load the data
t = pd.read_csv("Titanic-Dataset.csv", index_col=0)

# Separate features and target variable
X = t.drop(['Survived',], axis=1)  # Assuming 'Survived' is the target variable
y = t['Survived']

# Create a Logistic Regression model
lr = LogisticRegression()

# Create a StratifiedKFold object
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=23)

# Define the parameter grid for GridSearchCV
params = {
    'penalty': ['l1', 'l2', 'elasticnet', 'none'],
    'l1_ratio': [None] if 'l1' in ['l1', 'elasticnet'] else np.linspace(0, 1, 5),  # Adjusted line
    'solver': ['lbfgs', 'liblinear', 'newton-cg', 'sag', 'saga']
}

# Create GridSearchCV object
gcv = GridSearchCV(lr, param_grid=params, cv=kfold)
gcv.fit(X, y)

# Print the best parameters and score
print("Best Parameters:", gcv.best_params_)
print("Best Score:", gcv.best_score_)

# Create a DataFrame for the GridSearchCV results
pd_cv = pd.DataFrame(gcv.cv_results_)

# Fit the model with the best parameters on the whole dataset
best_model = LogisticRegression(**gcv.best_params_)
best_model.fit(X, y)

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:1182: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.
  warnings.warn(

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[19], line 32
     30 # Create GridSearchCV object
     31 gcv = GridSearchCV(lr, param_grid=params, cv=kfold)
---> 32 gcv.fit(X, y)
     34 # Print the best parameters and score
     35 print("Best Parameters:", gcv.best_params_)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1151, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1144     estimator._validate_params()
   1146 with config_context(
   1147     skip_parameter_validation=(
   1148         prefer_skip_nested_validation or global_skip_validation
   1149     )
   1150 ):
-> 1151     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1419, in GridSearchCV._run_search(self, evaluate_candidates)
   1417 def _run_search(self, evaluate_candidates):
   1418     """Search all candidates in param_grid"""
-> 1419     evaluate_candidates(ParameterGrid(self.param_grid))

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:875, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    868 elif len(out) != n_candidates * n_splits:
    869     raise ValueError(
    870         "cv.split and cv.get_n_splits returned "
    871         "inconsistent results. Expected {} "
    872         "splits, got {}".format(n_splits, len(out) // n_candidates)
    873     )
--> 875 _warn_or_raise_about_fit_failures(out, self.error_score)
    877 # For callable self.scoring, the return type is only know after
    878 # calling. If the return type is a dictionary, the error scores
    879 # can now be inserted with the correct key. The type checking
    880 # of out will be done in `_insert_error_scores`.
    881 if callable(self.scoring):

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:414, in _warn_or_raise_about_fit_failures(results, error_score)
    407 if num_failed_fits == num_fits:
    408     all_fits_failed_message = (
    409         f"\nAll the {num_fits} fits failed.\n"
    410         "It is very likely that your model is misconfigured.\n"
    411         "You can try to debug the error by setting error_score='raise'.\n\n"
    412         f"Below are more details about the failures:\n{fit_errors_summary}"
    413     )
--> 414     raise ValueError(all_fits_failed_message)
    416 else:
    417     some_fits_failed_message = (
    418         f"\n{num_failed_fits} fits failed out of a total of {num_fits}.\n"
    419         "The score on these train-test partitions for these parameters"
   (...)
    423         f"Below are more details about the failures:\n{fit_errors_summary}"
    424     )

ValueError: 
All the 100 fits failed.
It is very likely that your model is misconfigured.
You can try to debug the error by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1168, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
11 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1207, in fit
    X, y = self._validate_data(
           ^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 621, in _validate_data
    X, y = check_X_y(X, y, **check_params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\validation.py", line 1147, in check_X_y
    X = check_array(
        ^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\validation.py", line 917, in check_array
    array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_array_api.py", line 380, in _asarray_with_order
    array = numpy.asarray(array, order=order, dtype=dtype)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\pandas\core\generic.py", line 1998, in __array__
    arr = np.asarray(values, dtype=dtype)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: could not convert string to float: 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)'

--------------------------------------------------------------------------------
44 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1207, in fit
    X, y = self._validate_data(
           ^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 621, in _validate_data
    X, y = check_X_y(X, y, **check_params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\validation.py", line 1147, in check_X_y
    X = check_array(
        ^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\validation.py", line 917, in check_array
    array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_array_api.py", line 380, in _asarray_with_order
    array = numpy.asarray(array, order=order, dtype=dtype)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\pandas\core\generic.py", line 1998, in __array__
    arr = np.asarray(values, dtype=dtype)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: could not convert string to float: 'Braund, Mr. Owen Harris'

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1168, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1168, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1168, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1168, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 66, in _check_solver
    raise ValueError(
ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1168, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1168, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1178, in fit
    raise ValueError("l1_ratio must be specified when penalty is elasticnet.")
ValueError: l1_ratio must be specified when penalty is elasticnet.

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 732, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1151, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1168, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 73, in _check_solver
    raise ValueError("penalty='none' is not supported for the liblinear solver")
ValueError: penalty='none' is not supported for the liblinear solver


# Check for null values in the entire DataFrame
null_values = t.isnull().sum()

# Display the null values count for each column
print("Null Values in Each Column:")
print(null_values)

# Display the total count of null values in the DataFrame
total_null_count = null_values.sum()
print("\nTotal Null Values:", total_null_count)

Null Values in Each Column:
Survived      0
Pclass        0
Name          0
Sex           0
Age         177
SibSp         0
Parch         0
Ticket        0
Fare          0
Cabin       687
Embarked      2
dtype: int64

Total Null Values: 866


import seaborn as sns
sns.load_dataset('iris')


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import accuracy_score, log_loss
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder

# Set the working directory
import os
os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Bankruptcy")

# Load the dataset
bk = pd.read_csv('Bankruptcy.csv')

# Separate features and target variable
X = bk.drop('D', axis=1)
y = bk['D']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=23)

# Define the preprocessing steps for numerical and categorical features
numeric_features = X.select_dtypes(include=['float64', 'int64']).columns

# Create separate pipelines for StandardScaler and MinMaxScaler
numeric_transformer_standard = Pipeline(steps=[
    ('standard_scaler', StandardScaler())
])

numeric_transformer_minmax = Pipeline(steps=[
    ('minmax_scaler', MinMaxScaler())
])

categorical_features = X.select_dtypes(include=['object']).columns
categorical_transformer = Pipeline(steps=[
    ('onehot', OneHotEncoder(drop='first'))
])

# Combine the transformers using ColumnTransformer
preprocessor = ColumnTransformer(
    transformers=[
        ('num_standard', numeric_transformer_standard, numeric_features),
        ('num_minmax', numeric_transformer_minmax, numeric_features),
        ('cat', categorical_transformer, categorical_features)
    ])

# Create the pipeline with preprocessing and logistic regression steps
pipeline = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('classifier', LogisticRegression())
])

# Define the hyperparameter grid for logistic regression
param_grid = {
    'classifier__penalty': ['l1', 'l2'],
    'classifier__C': [0.001, 0.01, 0.1, 1, 10, 100]
}

# Use GridSearchCV for hyperparameter tuning
grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='neg_log_loss')
grid_search.fit(X_train, y_train)

# Get the best parameters and estimator from grid search
best_params = grid_search.best_params_
best_estimator = grid_search.best_estimator_

# Predictions
y_pred = best_estimator.predict(X_test)
y_predict_prob = best_estimator.predict_proba(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
logloss = log_loss(y_test, y_predict_prob)

print("Best Parameters:", best_params)
print("Accuracy:", accuracy)
print("Log Loss:", logloss)

Best Parameters: {'classifier__C': 10, 'classifier__penalty': 'l2'}
Accuracy: 0.95
Log Loss: 0.10152377748454318

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:425: FitFailedWarning: 
30 fits failed out of a total of 60.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
30 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 729, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\pipeline.py", line 427, in fit
    self._final_estimator.fit(Xt, y, **fit_params_last_step)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1169, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

  warnings.warn(some_fits_failed_message, FitFailedWarning)
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:979: UserWarning: One or more of the test scores are non-finite: [        nan -0.63978847         nan -0.48565813         nan -0.27250409
         nan -0.11501936         nan -0.0533569          nan -0.08157663]
  warnings.warn(


#bot scaler in one searc
pipe=Pipeline([('SCL',std_scaler),('SVM',svm)])
print(pipe.get_params())

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[13], line 2
      1 #bot scaler in one searc
----> 2 pipe=Pipeline([('SCL',std_scaler),('SVM',svm)])
      3 print(pipe.get_params())

NameError: name 'std_scaler' is not defined


from sklearn.tree import DecisionTreeClassifier,plot_tree
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay

hr=pd.read_csv('HR_comma_sep.csv')
dum_hr=pd.get_dummies(hr,drop_first=op_first=True)
x=dum_hr.drop('left',axis=1)
y=dum_hr


x


dtc=DecisionTreeClassifier(random_state=23,max_depth=2)
dtc.fit(x_train,y_train)

plt.figure(figsize=(10,8))
plot_tree(dtc,feature_names=list(x.columns),class_names=)

  Cell In[14], line 5
    dum_hr=pd.get_dummies(hr,drop_first=op_first=True)
                                                ^
SyntaxError: invalid syntax


from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import pandas as pd
import matplotlib.pyplot as plt
import os
os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\human-resources-analytics')

# Load the dataset
hr = pd.read_csv('HR_comma_sep.csv')

# Create dummy variables for categorical features
dum_hr = pd.get_dummies(hr, drop_first=True)

# Separate features (x) and target variable (y)
x = dum_hr.drop('left', axis=1)
y = dum_hr['left']

# Split the data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3,stratify=y, random_state=23)

# Create a Decision Tree Classifier with a maximum depth of 2
dtc = DecisionTreeClassifier(random_state=23, max_depth=2)

# Fit the model on the training data
dtc.fit(x_train, y_train)

# Plot the decision tree
plt.figure(figsize=(10, 8))
plot_tree(dtc, feature_names=list(x.columns), class_names=['Working', 'Left'], filled=True, rounded=True,fontsize=11)

plt.show()


x_train.shape

(10496, 18)


x_test.loc[256]

satisfaction_level         0.46
last_evaluation            0.46
number_project                2
average_montly_hours        154
time_spend_company            3
Work_accident                 0
promotion_last_5years         0
Department_RandD          False
Department_accounting      True
Department_hr             False
Department_management     False
Department_marketing      False
Department_product_mng    False
Department_sales          False
Department_support        False
Department_technical      False
salary_low                False
salary_medium              True
Name: 256, dtype: object


y_pred=dtc.predict(x_test)
y_pred_prob=dtc.predict_proba(x_test)
print(y_pred,
y_pred_prob
)

[0 1 0 ... 0 0 0] [[0.98597    0.01403   ]
 [0.12087027 0.87912973]
 [0.98597    0.01403   ]
 ...
 [0.98597    0.01403   ]
 [0.98597    0.01403   ]
 [0.54110079 0.45889921]]


os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Kyphosis')
kyph=pd.read_csv('kyphosis.csv')
y=kyph['Kyphosis']
x=kyph.drop('Kyphosis',axis=1)


x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3,stratify=y, random_state=23)

# Create a Decision Tree Classifier with a maximum depth of 5
dtc = DecisionTreeClassifier(random_state=23, max_depth=3)

# Fit the model on the training data
dtc.fit(x_train, y_train)

# Plot the decision tree
plt.figure(figsize=(10, 8))
plot_tree(dtc, feature_names=list(x.columns), class_names=['present', 'absent'], filled=True, rounded=True,fontsize=11)

plt.show()
y_pred=dtc.predict(x_test)
y_pred_prob=dtc.predict_proba(x_test)


print(f'accuracy score:{accuracy_score(y_test,y_pred)}')

accuracy score:0.8


from sklearn.model_selection import GridSearchCV,StratifiedKFold

kfold=StratifiedFold(n_splits=5,suffle=True,random_State=23)
param={'max_depth': [3, 4, 5, 6, 7, None]}
dtc=DecisionTreeClassifier(random_state=23)
gcv=GridSearchCV(dtc,param_grid=params,cv=kfold,scoring='neg_log_loss')
gcv.fit(x,y)

print(gcv.best_params_)
print(gcv.best_score_)

Best Parameters: {'max_depth': 4}

Accuracy Score: 0.68


from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import pandas as pd
import matplotlib.pyplot as plt
import os
os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\human-resources-analytics')

# Load the dataset
hr = pd.read_csv('HR_comma_sep.csv')

# Create dummy variables for categorical features
dum_hr = pd.get_dummies(hr, drop_first=True)

# Separate features (x) and target variable (y)
x = dum_hr.drop('left', axis=1)
y = dum_hr['left']

# Split the data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3,stratify=y, random_state=23)

# Create a Decision Tree Classifier with a maximum depth of 2
#dtc = DecisionTreeClassifier(random_state=23, max_depth=2)

# Fit the model on the training data
dtc.fit(x_train, y_train)

# Plot the decision tree
plt.figure(figsize=(6,4))
plot_tree(dtc, feature_names=list(x.columns), class_names=['Working', 'Left'], filled=True, rounded=True,fontsize=7)

plt.show()

# Define the grid of parameters to search
param_grid = {'max_depth': [3, 4, 5, 6, 7, None]}

# Create the GridSearchCV object
grid_search = GridSearchCV(dtc, param_grid, cv=5, scoring='accuracy', n_jobs=-1)

# Fit the grid search to the data
grid_search.fit(x_train, y_train)

# Get the best parameters from the grid search
best_params = grid_search.best_params_
print(f'Best Parameters: {best_params}')

# Get the best model from the grid search
best_model = grid_search.best_estimator_

# Plot the decision tree of the best model
plt.figure(figsize=(10, 8))
plot_tree(best_model, feature_names=list(x.columns), class_names=['absent', 'present'], filled=True, rounded=True, fontsize=11,)
plt.show()

# Predictions on the test set using the best model
y_pred = best_model.predict(x_test)

# Evaluate the accuracy of the best model
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy Score: {accuracy}')

print(y_pred)

Best Parameters: {'max_depth': 7}

Accuracy Score: 0.9768837519448766
[0 1 0 ... 0 0 1]


#usin rid search and log loss
from sklearn.model_selection import GridSearchCV,StratifiedKFold

kfold=StratifiedKFold(n_splits=5,shuffle=True,random_state=23)
params={'max_depth': [3, 4, 5, 6, 7, None]}
dtc=DecisionTreeClassifier(random_state=23)
gcv=GridSearchCV(dtc,param_grid=params,cv=kfold,scoring='neg_log_loss')
gcv.fit(x,y)

print(gcv.best_params_)
print(gcv.best_score_)

{'max_depth': 6}
-0.11458681615740332


#usin rid search and log loss
from sklearn.model_selection import GridSearchCV,StratifiedKFold

kfold=StratifiedKFold(n_splits=5,shuffle=True,random_state=23)
params={'min_samples_split':[2,5,10,20,60,80,100],'max_depth': [3, 4, 5, 6, 7, None],'min_samples_leaf':[1,5,10,20]}
dtc=DecisionTreeClassifier(random_state=23)
gcv=GridSearchCV(dtc,param_grid=params,cv=kfold,scoring='neg_log_loss')
gcv.fit(x,y)

print(gcv.best_params_)
print(gcv.best_score_)

{'max_depth': 7, 'min_samples_leaf': 5, 'min_samples_split': 100}
-0.09345373740479428


os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Kyphosis')
kyph=pd.read_csv('kyphosis.csv')
y=kyph['Kyphosis']
x=kyph.drop('Kyphosis',axis=1)


x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3,stratify=y, random_state=23)

# Create a Decision Tree Classifier with a maximum depth of 5
dtc = DecisionTreeClassifier(random_state=23, max_depth=3)

# Fit the model on the training data
dtc.fit(x_train, y_train)

# Plot the decision tree
plt.figure(figsize=(10, 8))
plot_tree(dtc, feature_names=list(x.columns), class_names=['present', 'absent'], filled=True, rounded=True,fontsize=11)

plt.show()
y_pred=dtc.predict(x_test)
y_pred_prob=dtc.predict_proba(x_test)


kfold=StratifiedKFold(n_splits=5,shuffle=True,random_state=23)
params={'min_samples_split':[2,5,10,20],'max_depth': [3, 4, 5, 6, 7, None],'min_samples_leaf':[1,5,10,20]}
dtc=DecisionTreeClassifier(random_state=23)
gcv=GridSearchCV(dtc,param_grid=params,cv=kfold,scoring='neg_log_loss')
gcv.fit(x,y)

print(gcv.best_params_)
print(gcv.best_score_)

{'max_depth': 3, 'min_samples_leaf': 20, 'min_samples_split': 2}
-1.643730404955098


bm_tree=gcv.best_estimator_
plt.figure(figsize=(20, 15))
plot_tree(bm_tree, feature_names=list(x.columns), class_names=['present', 'absent'], filled=True, rounded=True,fontsize=20)

plt.show()


from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

# Assuming x_train, y_train are your training data
# Assuming x_test, y_test are your testing data

svm_model = make_pipeline(StandardScaler(), SVC(kernel='linear', C=1))
stratified_kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=23)

svm_cv_scores = cross_val_score(svm_model, x_test, y_test, cv=stratified_kfold, scoring='accuracy')
print(f'SVM Mean Accuracy: {svm_cv_scores.mean()}')

SVM Mean Accuracy: 0.8


import os
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay
from sklearn.preprocessing import LabelEncoder

# Set the working directory
os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Glass Identification")

# Load the dataset
df = pd.read_csv('Glass.csv')

# Separate features (X) and target variable (y)
X = df.drop('Type', axis=1)
y = df['Type']

le=LabelEncoder()
le_y=

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,stratify=y, random_state=23)

# Define the classifier
dtc = DecisionTreeClassifier(random_state=42)

# Define the stratified k-fold cross-validator
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=23)

# Define the grid of parameters to search
params = {
    'min_samples_split': [2, 5, 10, 20, 60, 80, 100],
    'max_depth': [3, 4, 5, 6, 7, None],
    'min_samples_leaf': [1, 5, 10, 20]
}

# Create the GridSearchCV object
grid_search = GridSearchCV(dtc, param_grid=params, cv=kfold, scoring='neg_log_loss')

# Fit the grid search to the data
grid_search.fit(X_train, y_train)

# Get the best parameters from the grid search
best_params = grid_search.best_params_
print(f'Best Parameters: {best_params}')

# Get the best model from the grid search
best_model = grid_search.best_estimator_

# Plot the decision tree of the best model
plt.figure(figsize=(20, 10))
plot_tree(best_model, feature_names=list(X.columns), class_names=[str(cls) for cls in best_model.classes_],
          filled=True, fontsize=20)
plt.show()

# Predictions on the test set using the best model
y_pred = best_model.predict(X_test)

# Evaluate the accuracy of the best model
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy Score: {accuracy}')


print(y_pred)

Best Parameters: {'max_depth': 3, 'min_samples_leaf': 1, 'min_samples_split': 100}

Accuracy Score: 0.5230769230769231
['building_windows_float_processed' 'building_windows_float_processed'
 'building_windows_float_processed' 'building_windows_float_processed'
 'building_windows_non_float_processed' 'building_windows_float_processed'
 'building_windows_float_processed' 'building_windows_non_float_processed'
 'building_windows_non_float_processed' 'building_windows_float_processed'
 'headlamps' 'headlamps' 'building_windows_non_float_processed'
 'building_windows_float_processed' 'building_windows_float_processed'
 'headlamps' 'building_windows_float_processed'
 'building_windows_non_float_processed' 'building_windows_float_processed'
 'building_windows_float_processed' 'building_windows_float_processed'
 'headlamps' 'building_windows_non_float_processed'
 'building_windows_non_float_processed' 'building_windows_float_processed'
 'building_windows_float_processed' 'building_windows_float_processed'
 'building_windows_float_processed' 'building_windows_non_float_processed'
 'building_windows_float_processed' 'building_windows_non_float_processed'
 'building_windows_float_processed' 'building_windows_float_processed'
 'building_windows_non_float_processed' 'building_windows_float_processed'
 'building_windows_float_processed' 'building_windows_float_processed'
 'building_windows_float_processed' 'building_windows_non_float_processed'
 'building_windows_float_processed' 'building_windows_float_processed'
 'building_windows_non_float_processed' 'building_windows_float_processed'
 'headlamps' 'building_windows_float_processed'
 'building_windows_float_processed' 'building_windows_non_float_processed'
 'headlamps' 'building_windows_float_processed'
 'building_windows_float_processed' 'headlamps'
 'building_windows_float_processed' 'building_windows_float_processed'
 'building_windows_float_processed' 'building_windows_non_float_processed'
 'building_windows_float_processed' 'building_windows_float_processed'
 'building_windows_non_float_processed' 'building_windows_float_processed'
 'building_windows_float_processed' 'building_windows_float_processed'
 'building_windows_float_processed' 'building_windows_float_processed'
 'headlamps' 'building_windows_non_float_processed']


os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Datasets")
boston=pd.read_csv('Bsoton.csv')
x=boston.drop('medv',axis=1)
y=boston['medv']


x_train,x_test,y_train




dtr=Descision_treeReressor(random_stae=23,max_dept=2
                           dtr.fit(x_train,y_train)
print(r2_score(t_test))
                           
plt.figure(figsize=(20, 10))
plot_tree(best_model, feature_names=list(X.columns),
          filled=True, fontsize=20)
plt.show()

  Cell In[12], line 1
    boston=
           ^
SyntaxError: invalid syntax


import os
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor, plot_tree
from sklearn.metrics import r2_score
import matplotlib.pyplot as plt

# Set the working directory
os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Datasets")

# Load the Boston dataset
boston = pd.read_csv('Boston.csv')

# Separate features (X) and target variable (y)
X = boston.drop('medv', axis=1)
y = boston['medv']

# Split the data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=23)

# Define the Decision Tree Regressor
dtr = DecisionTreeRegressor(random_state=23, max_depth=2)

# Fit the model
dtr.fit(x_train, y_train)

# Make predictions on the test set
y_pred = dtr.predict(x_test)

# Evaluate the model using R2 score
r2 = r2_score(y_test, y_pred)
print(f'R2 Score: {r2}')

# Plot the decision tree
plt.figure(figsize=(20, 10))
plot_tree(dtr, feature_names=list(X.columns), filled=True, fontsize=20)
plt.show()

R2 Score: 0.5923085197343092


from sklearn.model_selection import KFold
# Define the grid of parameters to search
params = {
    'min_samples_split': [2, 5, 10, 20, 60, 80, 100],
    'max_depth': [3, 4, 5, 6, 7, None],
    'min_samples_leaf': [1, 5, 10, 20]
}
dtr=DecisionTreeRegressor(random_state=23)

kfold=KFold(n_splits=5,shuffle=True,random_state=23)

# Create the GridSearchCV object
gcv = GridSearchCV(dtr, param_grid=params, cv=kfold, scoring='r2',verbose=1)

# Fit the grid search to the data
gcv.fit(x_train, y_train)

# Get the best parameters from the grid search
best_params = gcv.best_params_
best_score=gcv.best_score_
print(f'Best Parameters: {best_params}')
print(f'best score: {best_score}')

# Get the best model from the grid search
best_model = grid_search.best_estimator_

# Plot the decision tree of the best model
plt.figure(figsize=(20, 10))
plot_tree(best_model, feature_names=list(X.columns),
          filled=True, fontsize=20)
plt.show()

Fitting 5 folds for each of 168 candidates, totalling 840 fits
Best Parameters: {'max_depth': None, 'min_samples_leaf': 10, 'min_samples_split': 2}
best score: 0.7548878539469166


from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import VotingClassifier
from sklearn.pipeline import Pipeline
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import pandas as pd
from sklearn.metrics import accuracy_score, log_loss
import matplotlib.pyplot as plt
import os
os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\human-resources-analytics')

# Load the dataset
hr = pd.read_csv('HR_comma_sep.csv')

# Create dummy variables for categorical features
dum_hr = pd.get_dummies(hr, drop_first=True)

# Separate features (x) and target variable (y)
x = dum_hr.drop('left', axis=1)
y = dum_hr['left']

# Split the data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3,stratify=y, random_state=23)

lr=LogisticRegression()
mm_scaler=MinMaxScaler()
knn=KNeighborsClassifier()
pipe_knn=Pipeline([('SCL',mm_scaler),('knn',knn)])
nb=GaussianNB()

voting=VotingClassifier([('LR',lr),('P_kNN',pipe_knn),('NB',nb)],voting='soft')
voting.fit(x_train,y_train)
y_pred=voting.predict(x_test)
print(accuracy_score(y_test,y_pred))


y_pred_proba=voting.predict_proba(x_test)
print(y_pred_prob)

print(log_loss(y_test,y_pred_proba))

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

0.8990886863747499
[0 1 0 ... 0 0 0]
0.2970896068063102


import numpy as np
from sklearn.model_selection import GridSearchCV,StratifiedKFold
kfold=KFold(n_splits=5,shuffle=True,random_state=23)
print(voting.get_params())
params={'LR_penalty':['l1','l2','elasticnet',None],'P_KNN_KNN_n_neighbors':[3,5,7],'NB_var_smoothing':np.linspace(1e-9,1,20)}

{'estimators': [('LR', LogisticRegression()), ('P_kNN', Pipeline(steps=[('SCL', MinMaxScaler()), ('knn', KNeighborsClassifier())])), ('NB', GaussianNB())], 'flatten_transform': True, 'n_jobs': None, 'verbose': False, 'voting': 'soft', 'weights': None, 'LR': LogisticRegression(), 'P_kNN': Pipeline(steps=[('SCL', MinMaxScaler()), ('knn', KNeighborsClassifier())]), 'NB': GaussianNB(), 'LR__C': 1.0, 'LR__class_weight': None, 'LR__dual': False, 'LR__fit_intercept': True, 'LR__intercept_scaling': 1, 'LR__l1_ratio': None, 'LR__max_iter': 100, 'LR__multi_class': 'auto', 'LR__n_jobs': None, 'LR__penalty': 'l2', 'LR__random_state': None, 'LR__solver': 'lbfgs', 'LR__tol': 0.0001, 'LR__verbose': 0, 'LR__warm_start': False, 'P_kNN__memory': None, 'P_kNN__steps': [('SCL', MinMaxScaler()), ('knn', KNeighborsClassifier())], 'P_kNN__verbose': False, 'P_kNN__SCL': MinMaxScaler(), 'P_kNN__knn': KNeighborsClassifier(), 'P_kNN__SCL__clip': False, 'P_kNN__SCL__copy': True, 'P_kNN__SCL__feature_range': (0, 1), 'P_kNN__knn__algorithm': 'auto', 'P_kNN__knn__leaf_size': 30, 'P_kNN__knn__metric': 'minkowski', 'P_kNN__knn__metric_params': None, 'P_kNN__knn__n_jobs': None, 'P_kNN__knn__n_neighbors': 5, 'P_kNN__knn__p': 2, 'P_kNN__knn__weights': 'uniform', 'NB__priors': None, 'NB__var_smoothing': 1e-09}


# Create GridSearchCV object
gcv = GridSearchCV(voting,param_grid=params,cv=kfold,scoring='neg_log_loss')
# Fit the grid search to the data
gcv.fit(x, y)

print(gcv.best_params_)
print(gcv.best_score_)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[54], line 4
      2 gcv = GridSearchCV(voting,param_grid=params,cv=kfold,scoring='neg_log_loss')
      3 # Fit the grid search to the data
----> 4 gcv.fit(x, y)
      6 print(gcv.best_params_)
      7 print(gcv.best_score_)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1422, in GridSearchCV._run_search(self, evaluate_candidates)
   1420 def _run_search(self, evaluate_candidates):
   1421     """Search all candidates in param_grid"""
-> 1422     evaluate_candidates(ParameterGrid(self.param_grid))

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:845, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    837 if self.verbose > 0:
    838     print(
    839         "Fitting {0} folds for each of {1} candidates,"
    840         " totalling {2} fits".format(
    841             n_splits, n_candidates, n_candidates * n_splits
    842         )
    843     )
--> 845 out = parallel(
    846     delayed(_fit_and_score)(
    847         clone(base_estimator),
    848         X,
    849         y,
    850         train=train,
    851         test=test,
    852         parameters=parameters,
    853         split_progress=(split_idx, n_splits),
    854         candidate_progress=(cand_idx, n_candidates),
    855         **fit_and_score_kwargs,
    856     )
    857     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    858         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    859     )
    860 )
    862 if len(out) < 1:
    863     raise ValueError(
    864         "No fits were performed. "
    865         "Was the CV iterator empty? "
    866         "Were there no candidates?"
    867     )

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1085, in Parallel.__call__(self, iterable)
   1076 try:
   1077     # Only set self._iterating to True if at least a batch
   1078     # was dispatched. In particular this covers the edge
   (...)
   1082     # was very quick and its callback already dispatched all the
   1083     # remaining jobs.
   1084     self._iterating = False
-> 1085     if self.dispatch_one_batch(iterator):
   1086         self._iterating = self._original_iterator is not None
   1088     while self.dispatch_one_batch(iterator):

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    710 fit_params = _check_fit_params(X, fit_params, train)
    712 if parameters is not None:
    713     # here we clone the parameters, since sometimes the parameters
    714     # themselves might be estimators, e.g. when we search over different
    715     # estimators in a pipeline.
    716     # ref: https://github.com/scikit-learn/scikit-learn/pull/26786
--> 717     estimator = estimator.set_params(**clone(parameters, safe=False))
    719 start_time = time.time()
    721 X_train, y_train = _safe_split(estimator, X, y, train)

File ~\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:312, in _BaseHeterogeneousEnsemble.set_params(self, **params)
    290 def set_params(self, **params):
    291     """
    292     Set the parameters of an estimator from the ensemble.
    293 
   (...)
    310         Estimator instance.
    311     """
--> 312     super()._set_params("estimators", **params)
    313     return self

File ~\anaconda3\Lib\site-packages\sklearn\utils\metaestimators.py:68, in _BaseComposition._set_params(self, attr, **params)
     65                 self._replace_estimator(attr, name, params.pop(name))
     67 # 3. Step parameters and other initialisation arguments
---> 68 super().set_params(**params)
     69 return self

File ~\anaconda3\Lib\site-packages\sklearn\base.py:230, in BaseEstimator.set_params(self, **params)
    228 if key not in valid_params:
    229     local_valid_params = self._get_param_names()
--> 230     raise ValueError(
    231         f"Invalid parameter {key!r} for estimator {self}. "
    232         f"Valid parameters are: {local_valid_params!r}."
    233     )
    235 if delim:
    236     nested_params[key][sub_key] = value

ValueError: Invalid parameter 'LR_penalty' for estimator VotingClassifier(estimators=[('LR', LogisticRegression()),
                             ('P_kNN',
                              Pipeline(steps=[('SCL', MinMaxScaler()),
                                              ('knn',
                                               KNeighborsClassifier())])),
                             ('NB', GaussianNB())],
                 voting='soft'). Valid parameters are: ['estimators', 'flatten_transform', 'n_jobs', 'verbose', 'voting', 'weights'].


from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold, KFold
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import VotingClassifier
from sklearn.pipeline import Pipeline
import pandas as pd
from sklearn.metrics import accuracy_score, log_loss

# Set the working directory
os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\human-resources-analytics')

# Load the dataset
hr = pd.read_csv('HR_comma_sep.csv')

# Create dummy variables for categorical features
dum_hr = pd.get_dummies(hr, drop_first=True)

# Separate features (x) and target variable (y)
x = dum_hr.drop('left', axis=1)
y = dum_hr['left']

# Split the data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, stratify=y, random_state=23)

lr = LogisticRegression()
mm_scaler = MinMaxScaler()
knn = KNeighborsClassifier()
pipe_knn = Pipeline([('SCL', mm_scaler), ('knn', knn)])
nb = GaussianNB()

voting = VotingClassifier([('LR', lr), ('P_kNN', pipe_knn), ('NB', nb)], voting='soft')
voting.fit(x_train, y_train)
y_pred = voting.predict(x_test)
print(f'Accuracy: {accuracy_score(y_test, y_pred)}')

y_pred_proba = voting.predict_proba(x_test)
print(y_pred_proba)

print(log_loss(y_test, y_pred_proba))

import numpy as np
from sklearn.model_selection import GridSearchCV, StratifiedKFold

kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=23)
print(voting.get_params())

params = {
    'LR__penalty': ['l1', 'l2', 'elasticnet', None],
    'P_kNN__knn__n_neighbors': [3, 5, 7],
    'NB__var_smoothing': np.linspace(1e-9, 1, 20)
}

# Create GridSearchCV object
gcv = GridSearchCV(voting, param_grid=params, cv=kfold, scoring='neg_log_loss')
# Fit the grid search to the data
gcv.fit(x_train, y_train)

print(gcv.best_params_)
print(gcv.best_score_)

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Accuracy: 0.8990886863747499
[[0.93138832 0.06861168]
 [0.24093017 0.75906983]
 [0.7417701  0.2582299 ]
 ...
 [0.99333082 0.00666918]
 [0.68483198 0.31516802]
 [0.52556073 0.47443927]]
0.2970896068063102
{'estimators': [('LR', LogisticRegression()), ('P_kNN', Pipeline(steps=[('SCL', MinMaxScaler()), ('knn', KNeighborsClassifier())])), ('NB', GaussianNB())], 'flatten_transform': True, 'n_jobs': None, 'verbose': False, 'voting': 'soft', 'weights': None, 'LR': LogisticRegression(), 'P_kNN': Pipeline(steps=[('SCL', MinMaxScaler()), ('knn', KNeighborsClassifier())]), 'NB': GaussianNB(), 'LR__C': 1.0, 'LR__class_weight': None, 'LR__dual': False, 'LR__fit_intercept': True, 'LR__intercept_scaling': 1, 'LR__l1_ratio': None, 'LR__max_iter': 100, 'LR__multi_class': 'auto', 'LR__n_jobs': None, 'LR__penalty': 'l2', 'LR__random_state': None, 'LR__solver': 'lbfgs', 'LR__tol': 0.0001, 'LR__verbose': 0, 'LR__warm_start': False, 'P_kNN__memory': None, 'P_kNN__steps': [('SCL', MinMaxScaler()), ('knn', KNeighborsClassifier())], 'P_kNN__verbose': False, 'P_kNN__SCL': MinMaxScaler(), 'P_kNN__knn': KNeighborsClassifier(), 'P_kNN__SCL__clip': False, 'P_kNN__SCL__copy': True, 'P_kNN__SCL__feature_range': (0, 1), 'P_kNN__knn__algorithm': 'auto', 'P_kNN__knn__leaf_size': 30, 'P_kNN__knn__metric': 'minkowski', 'P_kNN__knn__metric_params': None, 'P_kNN__knn__n_jobs': None, 'P_kNN__knn__n_neighbors': 5, 'P_kNN__knn__p': 2, 'P_kNN__knn__weights': 'uniform', 'NB__priors': None, 'NB__var_smoothing': 1e-09}

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:425: FitFailedWarning: 
600 fits failed out of a total of 1200.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
300 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 729, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_voting.py", line 349, in fit
    return super().fit(X, transformed_y, sample_weight)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_voting.py", line 81, in fit
    self.estimators_ = Parallel(n_jobs=self.n_jobs)(
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 65, in __call__
    return super().__call__(iterable_with_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 1085, in __call__
    if self.dispatch_one_batch(iterator):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 901, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 819, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
             ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 597, in __init__
    self.results = batch()
                   ^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in __call__
    return [func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in <listcomp>
    return [func(*args, **kwargs)
            ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 127, in __call__
    return self.function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py", line 36, in _fit_single_estimator
    estimator.fit(X, y)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1169, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
300 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 729, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_voting.py", line 349, in fit
    return super().fit(X, transformed_y, sample_weight)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_voting.py", line 81, in fit
    self.estimators_ = Parallel(n_jobs=self.n_jobs)(
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 65, in __call__
    return super().__call__(iterable_with_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 1085, in __call__
    if self.dispatch_one_batch(iterator):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 901, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 819, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
             ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 597, in __init__
    self.results = batch()
                   ^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in __call__
    return [func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in <listcomp>
    return [func(*args, **kwargs)
            ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 127, in __call__
    return self.function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py", line 36, in _fit_single_estimator
    estimator.fit(X, y)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1169, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  warnings.warn(some_fits_failed_message, FitFailedWarning)
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:979: UserWarning: One or more of the test scores are non-finite: [        nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
 -0.29451874 -0.30131555 -0.30648479 -0.29800708 -0.30470682 -0.30971574
 -0.29828389 -0.30499039 -0.31000356 -0.29850192 -0.30521453 -0.31023155
 -0.29869877 -0.30541665 -0.31043701 -0.29888152 -0.30560391 -0.31062714
 -0.29905262 -0.30577887 -0.31080457 -0.29921338 -0.30594293 -0.31097075
 -0.29936471 -0.30609709 -0.31112674 -0.29950738 -0.30624219 -0.31127341
 -0.29964205 -0.30637895 -0.31141152 -0.29976934 -0.30650802 -0.31154177
 -0.2998898  -0.30663002 -0.31166478 -0.30000393 -0.30674547 -0.3117811
 -0.30011221 -0.30685488 -0.31189126 -0.30021505 -0.30695869 -0.31199572
 -0.30031285 -0.30705731 -0.3120949  -0.30040596 -0.30715112 -0.31218919
 -0.30049471 -0.30724046 -0.31227894 -0.30057939 -0.30732564 -0.31236446
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
         nan         nan         nan         nan         nan         nan
 -0.29493692 -0.30171597 -0.30685106 -0.29799178 -0.30467835 -0.309684
 -0.29826654 -0.30495973 -0.30996951 -0.29848301 -0.30518217 -0.31019569
 -0.29867852 -0.30538284 -0.31039959 -0.29886007 -0.3055688  -0.31058833
 -0.2990301  -0.30574259 -0.31076451 -0.29918987 -0.30590559 -0.31092955
 -0.2993403  -0.30605878 -0.3110845  -0.29948214 -0.30620299 -0.31123021
 -0.29961605 -0.30633892 -0.31136744 -0.29974262 -0.30646723 -0.31149687
 -0.29986241 -0.30658852 -0.31161912 -0.29997592 -0.3067033  -0.31173473
 -0.30008362 -0.30681209 -0.31184423 -0.30018591 -0.30691531 -0.31194806
 -0.30028319 -0.30701339 -0.31204666 -0.30037582 -0.30710668 -0.3121404
 -0.3004641  -0.30719553 -0.31222962 -0.30054835 -0.30728025 -0.31231466]
  warnings.warn(

{'LR__penalty': 'l2', 'NB__var_smoothing': 1e-09, 'P_kNN__knn__n_neighbors': 3}
-0.2945187390074194

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


#1.	Consider dataset in cases/Satellite Imaging with y=classes. Try the following parameters on that data with tree classifier:
#a.	Min_samples_split: 2,3,4,5,6,10
#b.	Min_samples_leaf: 1,2,3,4,5,6,10
#c.	Max_depth: None, 2,3,4,5,6,7
#Find the best model on the basis of log loss


import os
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay
from sklearn.preprocessing import LabelEncoder

# Set the working directory
os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Satellite Imaging")

# Load the dataset
df = pd.read_csv('Satellite.csv',sep=';')

# Separate features (X) and target variable (y)
x = df.drop('classes', axis=1)
y = df['classes']

# Split the data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3,stratify=y, random_state=23)

# Define the classifier
dtc = DecisionTreeClassifier(random_state=23)

# Define the stratified k-fold cross-validator
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=23)

# Define the grid of parameters to search
params = {
    'min_samples_split': [2, 5, 10, 20, 60, 80, 100],
    'max_depth': [3, 4, 5, 6, 7, None],
    'min_samples_leaf': [1, 5, 10, 20]
}

# Create the GridSearchCV object
grid_search = GridSearchCV(dtc, param_grid=params, cv=kfold, scoring='neg_log_loss')

# Fit the grid search to the data
grid_search.fit(x_train, y_train)

# Get the best parameters from the grid search
best_params = grid_search.best_params_
print(f'Best Parameters: {best_params}')

# Get the best model from the grid search
best_model = grid_search.best_estimator_

# Plot the decision tree of the best model
plt.figure(figsize=(20, 10))
plot_tree(best_model, feature_names=list(x.columns), class_names=[str(cls) for cls in best_model.classes_],
          filled=True, fontsize=20)
plt.show()

# Predictions on the test set using the best model
y_pred = best_model.predict(x_test)

# Evaluate the accuracy of the best model
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy Score: {accuracy}')


print(y_pred)

Best Parameters: {'max_depth': 5, 'min_samples_leaf': 10, 'min_samples_split': 100}

Accuracy Score: 0.8187467633350596
['red soil' 'grey soil' 'very damp grey soil' ... 'vegetation stubble'
 'cotton crop' 'grey soil']


#a.	Decision Tree Regressor with parameters as:
#i.	Min_samples_split: 2,3,4,5,6,10, 20
#ii.	Min_samples_leaf: 1,2,3,4,5,6,10, 20
#iii.	Max_depth: None, 2,3,4,5,6,7
from sklearn.linear_model import LinearRegression,LogisticRegression
from sklearn.model_selection import cross_val_score, KFold, GridSearchCV
from sklearn.metrics import mean_squared_error,r2_score


os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Medical Cost Personal')
df=pd.read_csv('insurance.csv')

x=df.drop('charges',axis=1)
y=df['charges']


x_train,y_train,x_test,y_test=train_test_split(x,y,test_size=0.3,random_state=23)


lr = LogisticRegression()

# Fit the model
lr.fit(x_train, y_train)
print("Coefficients:", lr.coef_)
print("Intercept:", lr.intercept_)

# Predictions
y_pred = lr.predict(x_test)
y_predict_prob = lr.predict_proba(x_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

# GridSearchCV for Logistic Regression
penalties = ['l1', 'l2','elasticnet',None]
l1_ratios = np.linspace(0, 1, 5)
params_lr = {'penalty': penalties, 'l1_ratio': l1_ratios}

gcv_lr = GridSearchCV(lr, param_grid=params_lr, cv=5)
gcv_lr.fit(x_train, y_train)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_9968\3637359769.py in ?()
     19 
     20 lr = LogisticRegression()
     21 
     22 # Fit the model
---> 23 lr.fit(x_train, y_train)
     24 print("Coefficients:", lr.coef_)
     25 print("Intercept:", lr.intercept_)
     26 

~\anaconda3\Lib\site-packages\sklearn\base.py in ?(estimator, *args, **kwargs)
   1148                 skip_parameter_validation=(
   1149                     prefer_skip_nested_validation or global_skip_validation
   1150                 )
   1151             ):
-> 1152                 return fit_method(estimator, *args, **kwargs)

~\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py in ?(self, X, y, sample_weight)
   1204             _dtype = np.float64
   1205         else:
   1206             _dtype = [np.float64, np.float32]
   1207 
-> 1208         X, y = self._validate_data(
   1209             X,
   1210             y,
   1211             accept_sparse="csr",

~\anaconda3\Lib\site-packages\sklearn\base.py in ?(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)
    618                 if "estimator" not in check_y_params:
    619                     check_y_params = {**default_check_params, **check_y_params}
    620                 y = check_array(y, input_name="y", **check_y_params)
    621             else:
--> 622                 X, y = check_X_y(X, y, **check_params)
    623             out = X, y
    624 
    625         if not no_val_X and check_params.get("ensure_2d", True):

~\anaconda3\Lib\site-packages\sklearn\utils\validation.py in ?(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
   1142         raise ValueError(
   1143             f"{estimator_name} requires y to be passed, but the target y is None"
   1144         )
   1145 
-> 1146     X = check_array(
   1147         X,
   1148         accept_sparse=accept_sparse,
   1149         accept_large_sparse=accept_large_sparse,

~\anaconda3\Lib\site-packages\sklearn\utils\validation.py in ?(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)
    912                         )
    913                     array = xp.astype(array, dtype, copy=False)
    914                 else:
    915                     array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)
--> 916             except ComplexWarning as complex_warning:
    917                 raise ValueError(
    918                     "Complex data not supported\n{}\n".format(array)
    919                 ) from complex_warning

~\anaconda3\Lib\site-packages\sklearn\utils\_array_api.py in ?(array, dtype, order, copy, xp)
    376         # Use NumPy API to support order
    377         if copy is True:
    378             array = numpy.array(array, order=order, dtype=dtype)
    379         else:
--> 380             array = numpy.asarray(array, order=order, dtype=dtype)
    381 
    382         # At this point array is a NumPy ndarray. We convert it to an array
    383         # container that is consistent with the input's namespace.

~\anaconda3\Lib\site-packages\pandas\core\generic.py in ?(self, dtype)
   1996     def __array__(self, dtype: npt.DTypeLike | None = None) -> np.ndarray:
   1997         values = self._values
-> 1998         arr = np.asarray(values, dtype=dtype)
   1999         if (
   2000             astype_is_view(values.dtype, arr.dtype)
   2001             and using_copy_on_write()

ValueError: could not convert string to float: 'male'


from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
import os
import pandas as pd

os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Medical Cost Personal')
df = pd.read_csv('insurance.csv')

# Drop the 'charges' column for the features
x = df.drop('charges', axis=1)

# Encode categorical variables
x = pd.get_dummies(x, drop_first=True)

# Target variable
y = df['charges']

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=23)

# Use Linear Regression for predicting a continuous variable
lr = LinearRegression()

# Fit the model
lr.fit(x_train, y_train)

# Predictions
y_pred = lr.predict(x_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print("Mean Squared Error:", mse)
print("R-squared:", r2)

Mean Squared Error: 35313304.80808112
R-squared: 0.7292924601639752


# GridSearchCV for Logistic Regression
penalties = ['l1', 'l2','elasticnet',None]
l1_ratios = np.linspace(0, 1, 5)
params_lr = {'penalty': penalties, 'l1_ratio': l1_ratios}

gcv_lr = GridSearchCV(lr, param_grid=params_lr, cv=5)
gcv_lr.fit(x_train, y_train)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[89], line 7
      4 params_lr = {'penalty': penalties, 'l1_ratio': l1_ratios}
      6 gcv_lr = GridSearchCV(lr, param_grid=params_lr, cv=5)
----> 7 gcv_lr.fit(x_train, y_train)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1422, in GridSearchCV._run_search(self, evaluate_candidates)
   1420 def _run_search(self, evaluate_candidates):
   1421     """Search all candidates in param_grid"""
-> 1422     evaluate_candidates(ParameterGrid(self.param_grid))

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:845, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    837 if self.verbose > 0:
    838     print(
    839         "Fitting {0} folds for each of {1} candidates,"
    840         " totalling {2} fits".format(
    841             n_splits, n_candidates, n_candidates * n_splits
    842         )
    843     )
--> 845 out = parallel(
    846     delayed(_fit_and_score)(
    847         clone(base_estimator),
    848         X,
    849         y,
    850         train=train,
    851         test=test,
    852         parameters=parameters,
    853         split_progress=(split_idx, n_splits),
    854         candidate_progress=(cand_idx, n_candidates),
    855         **fit_and_score_kwargs,
    856     )
    857     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    858         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    859     )
    860 )
    862 if len(out) < 1:
    863     raise ValueError(
    864         "No fits were performed. "
    865         "Was the CV iterator empty? "
    866         "Were there no candidates?"
    867     )

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1085, in Parallel.__call__(self, iterable)
   1076 try:
   1077     # Only set self._iterating to True if at least a batch
   1078     # was dispatched. In particular this covers the edge
   (...)
   1082     # was very quick and its callback already dispatched all the
   1083     # remaining jobs.
   1084     self._iterating = False
-> 1085     if self.dispatch_one_batch(iterator):
   1086         self._iterating = self._original_iterator is not None
   1088     while self.dispatch_one_batch(iterator):

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    710 fit_params = _check_fit_params(X, fit_params, train)
    712 if parameters is not None:
    713     # here we clone the parameters, since sometimes the parameters
    714     # themselves might be estimators, e.g. when we search over different
    715     # estimators in a pipeline.
    716     # ref: https://github.com/scikit-learn/scikit-learn/pull/26786
--> 717     estimator = estimator.set_params(**clone(parameters, safe=False))
    719 start_time = time.time()
    721 X_train, y_train = _safe_split(estimator, X, y, train)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:230, in BaseEstimator.set_params(self, **params)
    228 if key not in valid_params:
    229     local_valid_params = self._get_param_names()
--> 230     raise ValueError(
    231         f"Invalid parameter {key!r} for estimator {self}. "
    232         f"Valid parameters are: {local_valid_params!r}."
    233     )
    235 if delim:
    236     nested_params[key][sub_key] = value

ValueError: Invalid parameter 'l1_ratio' for estimator LinearRegression(). Valid parameters are: ['copy_X', 'fit_intercept', 'n_jobs', 'positive'].


# Define the hyperparameters to search
params = {
    'fit_intercept': [True, False],
    'normalize': [True, False],
    'copy_X': [True, False]
}

# Create the GridSearchCV object
grid_search = GridSearchCV(lr, param_grid=params, scoring='neg_mean_squared_error', cv=5)

# Fit the grid search to the data
grid_search.fit(x_train, y_train)

# Get the best parameters from the grid search
best_params = grid_search.best_params_
print(f'Best Parameters: {best_params}')

# Get the best model from the grid search
best_model = grid_search.best_estimator_

# Predictions on the test set using the best model
y_pred = best_model.predict(x_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print("Mean Squared Error:", mse)
print("R-squared:", r2)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[91], line 12
      9 grid_search = GridSearchCV(lr, param_grid=params, scoring='neg_mean_squared_error', cv=5)
     11 # Fit the grid search to the data
---> 12 grid_search.fit(x_train, y_train)
     14 # Get the best parameters from the grid search
     15 best_params = grid_search.best_params_

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1422, in GridSearchCV._run_search(self, evaluate_candidates)
   1420 def _run_search(self, evaluate_candidates):
   1421     """Search all candidates in param_grid"""
-> 1422     evaluate_candidates(ParameterGrid(self.param_grid))

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:845, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    837 if self.verbose > 0:
    838     print(
    839         "Fitting {0} folds for each of {1} candidates,"
    840         " totalling {2} fits".format(
    841             n_splits, n_candidates, n_candidates * n_splits
    842         )
    843     )
--> 845 out = parallel(
    846     delayed(_fit_and_score)(
    847         clone(base_estimator),
    848         X,
    849         y,
    850         train=train,
    851         test=test,
    852         parameters=parameters,
    853         split_progress=(split_idx, n_splits),
    854         candidate_progress=(cand_idx, n_candidates),
    855         **fit_and_score_kwargs,
    856     )
    857     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    858         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    859     )
    860 )
    862 if len(out) < 1:
    863     raise ValueError(
    864         "No fits were performed. "
    865         "Was the CV iterator empty? "
    866         "Were there no candidates?"
    867     )

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1085, in Parallel.__call__(self, iterable)
   1076 try:
   1077     # Only set self._iterating to True if at least a batch
   1078     # was dispatched. In particular this covers the edge
   (...)
   1082     # was very quick and its callback already dispatched all the
   1083     # remaining jobs.
   1084     self._iterating = False
-> 1085     if self.dispatch_one_batch(iterator):
   1086         self._iterating = self._original_iterator is not None
   1088     while self.dispatch_one_batch(iterator):

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    710 fit_params = _check_fit_params(X, fit_params, train)
    712 if parameters is not None:
    713     # here we clone the parameters, since sometimes the parameters
    714     # themselves might be estimators, e.g. when we search over different
    715     # estimators in a pipeline.
    716     # ref: https://github.com/scikit-learn/scikit-learn/pull/26786
--> 717     estimator = estimator.set_params(**clone(parameters, safe=False))
    719 start_time = time.time()
    721 X_train, y_train = _safe_split(estimator, X, y, train)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:230, in BaseEstimator.set_params(self, **params)
    228 if key not in valid_params:
    229     local_valid_params = self._get_param_names()
--> 230     raise ValueError(
    231         f"Invalid parameter {key!r} for estimator {self}. "
    232         f"Valid parameters are: {local_valid_params!r}."
    233     )
    235 if delim:
    236     nested_params[key][sub_key] = value

ValueError: Invalid parameter 'normalize' for estimator LinearRegression(). Valid parameters are: ['copy_X', 'fit_intercept', 'n_jobs', 'positive'].


os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Medical Cost Personal')
df=pd.read_csv('insurance.csv')
df.columns

Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges'], dtype='object')


import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import ElasticNet
from sklearn.ensemble import VotingRegressor
from sklearn.metrics import r2_score

# Load the dataset
df = pd.read_csv('insurance.csv')

# Encode categorical variables
df_encoded = pd.get_dummies(df, columns=['sex', 'smoker', 'region'], drop_first=True)

# Separate features (X) and target variable (y)
X = df_encoded.drop('charges', axis=1)
y = df_encoded['charges']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=23)

# Task 3a: Decision Tree Regressor
param_grid_decision_tree = {
    'min_samples_split': [2, 3, 4, 5, 6, 10, 20],
    'min_samples_leaf': [1, 2, 3, 4, 5, 6, 10, 20],
    'max_depth': [None, 2, 3, 4, 5, 6, 7]
}

dt_regressor = DecisionTreeRegressor()
grid_search_decision_tree = GridSearchCV(dt_regressor, param_grid_decision_tree, cv=5)
grid_search_decision_tree.fit(X_train, y_train)

# ... (continue with the rest of your code)

GridSearchCV(cv=5, estimator=DecisionTreeRegressor(),
             param_grid={'max_depth': [None, 2, 3, 4, 5, 6, 7],
                         'min_samples_leaf': [1, 2, 3, 4, 5, 6, 10, 20],
                         'min_samples_split': [2, 3, 4, 5, 6, 10, 20]})

GridSearchCV(cv=5, estimator=DecisionTreeRegressor(),
             param_grid={'max_depth': [None, 2, 3, 4, 5, 6, 7],
                         'min_samples_leaf': [1, 2, 3, 4, 5, 6, 10, 20],
                         'min_samples_split': [2, 3, 4, 5, 6, 10, 20]})

DecisionTreeRegressor()

DecisionTreeRegressor()


# Get the best model from the grid search
best_decision_tree_model = grid_search_decision_tree.best_estimator_

# Predictions using the best model
y_pred_decision_tree = best_decision_tree_model.predict(X_test)

# Evaluate the model
r2_decision_tree = r2_score(y_test, y_pred_decision_tree)
print("R-squared for Decision Tree Regressor:", r2_decision_tree)

# Task 3b: ElasticNet Regression
param_grid_elastic_net = {
    'alpha': np.linspace(0.0001, 5, 10),
    'l1_ratio': np.linspace(0, 1, 10)
}

elastic_net = ElasticNet()
grid_search_elastic_net = GridSearchCV(elastic_net, param_grid_elastic_net, cv=5)
grid_search_elastic_net.fit(X_train, y_train)

# Get the best model from the grid search
best_elastic_net_model = grid_search_elastic_net.best_estimator_

# Predictions using the best model
y_pred_elastic_net = best_elastic_net_model.predict(X_test)

# Evaluate the model
r2_elastic_net = r2_score(y_test, y_pred_elastic_net)
print("R-squared for ElasticNet Regression:", r2_elastic_net)

# Task 3c: Voting Regression
elastic_net_params = {
    'l1_ratio': np.linspace(0.001, 1, 10),
    'alpha': np.linspace(0, 3, 5)
}

decision_tree_params = {
    'min_samples_split': [2, 4, 7, 10],
    'min_samples_leaf': [1, 4, 7, 10],
    'max_depth': [None, 3, 5]
}

elastic_net_regressor = ElasticNet()
dt_regressor = DecisionTreeRegressor()

voting_regressor = VotingRegressor(estimators=[
    ('elastic_net', elastic_net_regressor.set_params(**elastic_net_params)),
    ('decision_tree', dt_regressor.set_params(**decision_tree_params))
])

voting_regressor.fit(X_train, y_train)

# Predictions using the voting regressor
y_pred_voting = voting_regressor.predict(X_test)

# Evaluate the model
r2_voting = r2_score(y_test, y_pred_voting)
print("R-squared for Voting Regression:", r2_voting)

R-squared for Decision Tree Regressor: 0.8271211214798534

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.405e+10, tolerance: 1.139e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.396e+10, tolerance: 1.125e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.418e+10, tolerance: 1.142e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.344e+10, tolerance: 1.162e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.389e+10, tolerance: 1.161e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.189e+10, tolerance: 1.139e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.180e+10, tolerance: 1.125e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.229e+10, tolerance: 1.142e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.265e+10, tolerance: 1.162e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.186e+10, tolerance: 1.161e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.557e+10, tolerance: 1.139e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.550e+10, tolerance: 1.125e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.600e+10, tolerance: 1.142e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.649e+10, tolerance: 1.162e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.563e+10, tolerance: 1.161e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.705e+10, tolerance: 1.139e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.698e+10, tolerance: 1.125e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.749e+10, tolerance: 1.142e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.803e+10, tolerance: 1.162e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.715e+10, tolerance: 1.161e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.787e+10, tolerance: 1.139e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.780e+10, tolerance: 1.125e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.831e+10, tolerance: 1.142e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.887e+10, tolerance: 1.162e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.799e+10, tolerance: 1.161e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.839e+10, tolerance: 1.139e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.832e+10, tolerance: 1.125e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.883e+10, tolerance: 1.142e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.941e+10, tolerance: 1.162e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.853e+10, tolerance: 1.161e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.876e+10, tolerance: 1.139e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.868e+10, tolerance: 1.125e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.919e+10, tolerance: 1.142e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.979e+10, tolerance: 1.162e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.891e+10, tolerance: 1.161e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.904e+10, tolerance: 1.139e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.896e+10, tolerance: 1.125e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.947e+10, tolerance: 1.142e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.007e+10, tolerance: 1.162e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.920e+10, tolerance: 1.161e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.925e+10, tolerance: 1.139e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.917e+10, tolerance: 1.125e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.968e+10, tolerance: 1.142e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.029e+10, tolerance: 1.162e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.943e+10, tolerance: 1.161e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.943e+10, tolerance: 1.139e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.935e+10, tolerance: 1.125e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.986e+10, tolerance: 1.142e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.047e+10, tolerance: 1.162e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.962e+10, tolerance: 1.161e+07 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
  model = cd_fast.enet_coordinate_descent(

R-squared for ElasticNet Regression: 0.7296453581082869

---------------------------------------------------------------------------
InvalidParameterError                     Traceback (most recent call last)
Cell In[99], line 51
     44 dt_regressor = DecisionTreeRegressor()
     46 voting_regressor = VotingRegressor(estimators=[
     47     ('elastic_net', elastic_net_regressor.set_params(**elastic_net_params)),
     48     ('decision_tree', dt_regressor.set_params(**decision_tree_params))
     49 ])
---> 51 voting_regressor.fit(X_train, y_train)
     53 # Predictions using the voting regressor
     54 y_pred_voting = voting_regressor.predict(X_test)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\ensemble\_voting.py:605, in VotingRegressor.fit(self, X, y, sample_weight)
    583 """Fit the estimators.
    584 
    585 Parameters
   (...)
    602     Fitted estimator.
    603 """
    604 y = column_or_1d(y, warn=True)
--> 605 return super().fit(X, y, sample_weight)

File ~\anaconda3\Lib\site-packages\sklearn\ensemble\_voting.py:81, in _BaseVoting.fit(self, X, y, sample_weight)
     75 if self.weights is not None and len(self.weights) != len(self.estimators):
     76     raise ValueError(
     77         "Number of `estimators` and weights must be equal; got"
     78         f" {len(self.weights)} weights, {len(self.estimators)} estimators"
     79     )
---> 81 self.estimators_ = Parallel(n_jobs=self.n_jobs)(
     82     delayed(_fit_single_estimator)(
     83         clone(clf),
     84         X,
     85         y,
     86         sample_weight=sample_weight,
     87         message_clsname="Voting",
     88         message=self._log_message(names[idx], idx + 1, len(clfs)),
     89     )
     90     for idx, clf in enumerate(clfs)
     91     if clf != "drop"
     92 )
     94 self.named_estimators_ = Bunch()
     96 # Uses 'drop' as placeholder for dropped estimators

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1085, in Parallel.__call__(self, iterable)
   1076 try:
   1077     # Only set self._iterating to True if at least a batch
   1078     # was dispatched. In particular this covers the edge
   (...)
   1082     # was very quick and its callback already dispatched all the
   1083     # remaining jobs.
   1084     self._iterating = False
-> 1085     if self.dispatch_one_batch(iterator):
   1086         self._iterating = self._original_iterator is not None
   1088     while self.dispatch_one_batch(iterator):

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:36, in _fit_single_estimator(estimator, X, y, sample_weight, message_clsname, message)
     34 else:
     35     with _print_elapsed_time(message_clsname, message):
---> 36         estimator.fit(X, y)
     37 return estimator

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1145, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1140 partial_fit_and_fitted = (
   1141     fit_method.__name__ == "partial_fit" and _is_fitted(estimator)
   1142 )
   1144 if not global_skip_validation and not partial_fit_and_fitted:
-> 1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
   1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:638, in BaseEstimator._validate_params(self)
    630 def _validate_params(self):
    631     """Validate types and values of constructor parameters
    632 
    633     The expected type and values must be defined in the `_parameter_constraints`
   (...)
    636     accepted constraints.
    637     """
--> 638     validate_parameter_constraints(
    639         self._parameter_constraints,
    640         self.get_params(deep=False),
    641         caller_name=self.__class__.__name__,
    642     )

File ~\anaconda3\Lib\site-packages\sklearn\utils\_param_validation.py:96, in validate_parameter_constraints(parameter_constraints, params, caller_name)
     90 else:
     91     constraints_str = (
     92         f"{', '.join([str(c) for c in constraints[:-1]])} or"
     93         f" {constraints[-1]}"
     94     )
---> 96 raise InvalidParameterError(
     97     f"The {param_name!r} parameter of {caller_name} must be"
     98     f" {constraints_str}. Got {param_val!r} instead."
     99 )

InvalidParameterError: The 'alpha' parameter of ElasticNet must be a float in the range [0.0, inf). Got array([0.  , 0.75, 1.5 , 2.25, 3.  ]) instead.


import pandas as pd 
from sklearn.linear_model import LogisticRegression 
from sklearn.naive_bayes import GaussianNB 
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import BaggingClassifier
import matplotlib.pyplot as plt 
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, log_loss
import numpy as np
from sklearn.pipeline import Pipeline
import os
os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\human-resources-analytics')

hr = pd.read_csv("HR_comma_sep.csv")
dum_hr = pd.get_dummies(hr, drop_first=True)
X = dum_hr.drop('left', axis=1)
y = dum_hr['left']

X_train, X_test, y_train, y_test = train_test_split(X,y, 
                               test_size=0.3,
                               stratify=y,
                               random_state=23)
lr = LogisticRegression()

bagg = BaggingClassifier(lr, n_estimators=15)
bagg.fit(X_train, y_train)
y_pred = bagg.predict(X_test)
print(accuracy_score(y_test, y_pred))
y_pred_prob = bagg.predict_proba(X_test)
print(log_loss(y_test, y_pred_prob))

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

0.8017337186041342
0.4331039723821298

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


#grid search cv

from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GridSearchCV

kfold=StratifiedKFold(n_splits=5,shuffle=True,random_state=23)
print(bagg.get_params())
bagg=BaggingClassifier(lr,n_estimators=15,random_state=23)
params={'estimator_penalty':['l1','l2','elasticnet',None]}
gcv=GridSearchCV(bagg,param_grid=params,cv=kfold,scoring='neg_log_loss')

gcv.fit(X,y)
print(gcv_best_params_)
print(gcv.best_score_)

{'base_estimator': 'deprecated', 'bootstrap': True, 'bootstrap_features': False, 'estimator__C': 1.0, 'estimator__class_weight': None, 'estimator__dual': False, 'estimator__fit_intercept': True, 'estimator__intercept_scaling': 1, 'estimator__l1_ratio': None, 'estimator__max_iter': 100, 'estimator__multi_class': 'auto', 'estimator__n_jobs': None, 'estimator__penalty': 'l2', 'estimator__random_state': None, 'estimator__solver': 'lbfgs', 'estimator__tol': 0.0001, 'estimator__verbose': 0, 'estimator__warm_start': False, 'estimator': LogisticRegression(), 'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 15, 'n_jobs': None, 'oob_score': False, 'random_state': 23, 'verbose': 0, 'warm_start': False}

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[5], line 12
      9 params={'estimator_penalty':['l1','l2','elasticnet',None]}
     10 gcv=GridSearchCV(bagg,param_grid=params,cv=kfold,scoring='neg_log_loss')
---> 12 gcv.fit(X,y)
     13 print(gcv_best_params_)
     14 print(gcv.best_score_)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1422, in GridSearchCV._run_search(self, evaluate_candidates)
   1420 def _run_search(self, evaluate_candidates):
   1421     """Search all candidates in param_grid"""
-> 1422     evaluate_candidates(ParameterGrid(self.param_grid))

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:845, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    837 if self.verbose > 0:
    838     print(
    839         "Fitting {0} folds for each of {1} candidates,"
    840         " totalling {2} fits".format(
    841             n_splits, n_candidates, n_candidates * n_splits
    842         )
    843     )
--> 845 out = parallel(
    846     delayed(_fit_and_score)(
    847         clone(base_estimator),
    848         X,
    849         y,
    850         train=train,
    851         test=test,
    852         parameters=parameters,
    853         split_progress=(split_idx, n_splits),
    854         candidate_progress=(cand_idx, n_candidates),
    855         **fit_and_score_kwargs,
    856     )
    857     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    858         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    859     )
    860 )
    862 if len(out) < 1:
    863     raise ValueError(
    864         "No fits were performed. "
    865         "Was the CV iterator empty? "
    866         "Were there no candidates?"
    867     )

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1085, in Parallel.__call__(self, iterable)
   1076 try:
   1077     # Only set self._iterating to True if at least a batch
   1078     # was dispatched. In particular this covers the edge
   (...)
   1082     # was very quick and its callback already dispatched all the
   1083     # remaining jobs.
   1084     self._iterating = False
-> 1085     if self.dispatch_one_batch(iterator):
   1086         self._iterating = self._original_iterator is not None
   1088     while self.dispatch_one_batch(iterator):

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    710 fit_params = _check_fit_params(X, fit_params, train)
    712 if parameters is not None:
    713     # here we clone the parameters, since sometimes the parameters
    714     # themselves might be estimators, e.g. when we search over different
    715     # estimators in a pipeline.
    716     # ref: https://github.com/scikit-learn/scikit-learn/pull/26786
--> 717     estimator = estimator.set_params(**clone(parameters, safe=False))
    719 start_time = time.time()
    721 X_train, y_train = _safe_split(estimator, X, y, train)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:230, in BaseEstimator.set_params(self, **params)
    228 if key not in valid_params:
    229     local_valid_params = self._get_param_names()
--> 230     raise ValueError(
    231         f"Invalid parameter {key!r} for estimator {self}. "
    232         f"Valid parameters are: {local_valid_params!r}."
    233     )
    235 if delim:
    236     nested_params[key][sub_key] = value

ValueError: Invalid parameter 'estimator_penalty' for estimator BaggingClassifier(estimator=LogisticRegression(), n_estimators=15,
                  random_state=23). Valid parameters are: ['base_estimator', 'bootstrap', 'bootstrap_features', 'estimator', 'max_features', 'max_samples', 'n_estimators', 'n_jobs', 'oob_score', 'random_state', 'verbose', 'warm_start'].


from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import BaggingClassifier

# Assuming lr is already defined

# Create BaggingClassifier
bagg = BaggingClassifier(base_estimator=lr, n_estimators=15, random_state=23)
print(bagg.get_params())  # Print parameters before GridSearchCV

# Define StratifiedKFold
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=23)

# Define the parameter grid
params = {'base_estimator__penalty': ['l1', 'l2', 'elasticnet', None]}

# Create GridSearchCV instance
gcv = GridSearchCV(bagg, param_grid=params, cv=kfold, scoring='neg_log_loss')

# Fit the model
gcv.fit(X, y)

# Print the best parameters and best score
print(gcv.best_params_)
print(gcv.best_score_)

{'base_estimator__C': 1.0, 'base_estimator__class_weight': None, 'base_estimator__dual': False, 'base_estimator__fit_intercept': True, 'base_estimator__intercept_scaling': 1, 'base_estimator__l1_ratio': None, 'base_estimator__max_iter': 100, 'base_estimator__multi_class': 'auto', 'base_estimator__n_jobs': None, 'base_estimator__penalty': 'l2', 'base_estimator__random_state': None, 'base_estimator__solver': 'lbfgs', 'base_estimator__tol': 0.0001, 'base_estimator__verbose': 0, 'base_estimator__warm_start': False, 'base_estimator': LogisticRegression(), 'bootstrap': True, 'bootstrap_features': False, 'estimator': None, 'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 15, 'n_jobs': None, 'oob_score': False, 'random_state': 23, 'verbose': 0, 'warm_start': False}

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:425: FitFailedWarning: 
10 fits failed out of a total of 20.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 729, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 338, in fit
    return self._fit(X, y, self.max_samples, sample_weight=sample_weight)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 473, in _fit
    all_results = Parallel(
                  ^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 65, in __call__
    return super().__call__(iterable_with_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 1085, in __call__
    if self.dispatch_one_batch(iterator):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 901, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 819, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
             ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 597, in __init__
    self.results = batch()
                   ^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in __call__
    return [func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in <listcomp>
    return [func(*args, **kwargs)
            ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 127, in __call__
    return self.function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 141, in _parallel_build_estimators
    estimator_fit(X_, y, sample_weight=curr_sample_weight)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1169, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 729, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 338, in fit
    return self._fit(X, y, self.max_samples, sample_weight=sample_weight)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 473, in _fit
    all_results = Parallel(
                  ^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 65, in __call__
    return super().__call__(iterable_with_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 1085, in __call__
    if self.dispatch_one_batch(iterator):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 901, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 819, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
             ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 597, in __init__
    self.results = batch()
                   ^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in __call__
    return [func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in <listcomp>
    return [func(*args, **kwargs)
            ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 127, in __call__
    return self.function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 141, in _parallel_build_estimators
    estimator_fit(X_, y, sample_weight=curr_sample_weight)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1169, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  warnings.warn(some_fits_failed_message, FitFailedWarning)
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:979: UserWarning: One or more of the test scores are non-finite: [        nan -0.43098534         nan -0.43123255]
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

{'base_estimator__penalty': 'l2'}
-0.43098533602002503

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


import pandas as pd 
from sklearn.linear_model import LogisticRegression 
from sklearn.naive_bayes import GaussianNB 
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import BaggingClassifier
import matplotlib.pyplot as plt 
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, log_loss
import numpy as np
from sklearn.pipeline import Pipeline
import os
os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\human-resources-analytics')


hr = pd.read_csv("HR_comma_sep.csv")
dum_hr = pd.get_dummies(hr, drop_first=True)
X = dum_hr.drop('left', axis=1)
y = dum_hr['left']

X_train, X_test, y_train, y_test = train_test_split(X,y, 
                               test_size=0.3,
                               stratify=y,
                               random_state=23)
lr = LogisticRegression()

bagg = BaggingClassifier(lr, n_estimators=15,
                         random_state=23)
bagg.fit(X_train, y_train)
y_pred = bagg.predict(X_test)
print(accuracy_score(y_test, y_pred))
y_pred_prob = bagg.predict_proba(X_test)
print(log_loss(y_test, y_pred_prob))

############## Grid Search #################
from sklearn.model_selection import StratifiedKFold 
from sklearn.model_selection import GridSearchCV

kfold = StratifiedKFold(n_splits=5, 
                        shuffle=True, random_state=23)
print(bagg.get_params())
bagg = BaggingClassifier(lr, n_estimators=15,
                         random_state=23)
params = {'estimator__penalty':['l1','l2','elasticnet',None]}
gcv = GridSearchCV(bagg, param_grid=params,
                   cv=kfold, scoring='neg_log_loss')
gcv.fit(X, y)
print(gcv.best_params_)
print(gcv.best_score_)

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

0.8017337186041342
0.43427987931266165
{'base_estimator': 'deprecated', 'bootstrap': True, 'bootstrap_features': False, 'estimator__C': 1.0, 'estimator__class_weight': None, 'estimator__dual': False, 'estimator__fit_intercept': True, 'estimator__intercept_scaling': 1, 'estimator__l1_ratio': None, 'estimator__max_iter': 100, 'estimator__multi_class': 'auto', 'estimator__n_jobs': None, 'estimator__penalty': 'l2', 'estimator__random_state': None, 'estimator__solver': 'lbfgs', 'estimator__tol': 0.0001, 'estimator__verbose': 0, 'estimator__warm_start': False, 'estimator': LogisticRegression(), 'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 15, 'n_jobs': None, 'oob_score': False, 'random_state': 23, 'verbose': 0, 'warm_start': False}

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:425: FitFailedWarning: 
10 fits failed out of a total of 20.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 729, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 338, in fit
    return self._fit(X, y, self.max_samples, sample_weight=sample_weight)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 473, in _fit
    all_results = Parallel(
                  ^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 65, in __call__
    return super().__call__(iterable_with_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 1085, in __call__
    if self.dispatch_one_batch(iterator):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 901, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 819, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
             ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 597, in __init__
    self.results = batch()
                   ^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in __call__
    return [func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in <listcomp>
    return [func(*args, **kwargs)
            ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 127, in __call__
    return self.function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 141, in _parallel_build_estimators
    estimator_fit(X_, y, sample_weight=curr_sample_weight)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1169, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 729, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 338, in fit
    return self._fit(X, y, self.max_samples, sample_weight=sample_weight)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 473, in _fit
    all_results = Parallel(
                  ^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 65, in __call__
    return super().__call__(iterable_with_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 1085, in __call__
    if self.dispatch_one_batch(iterator):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 901, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 819, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
             ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 597, in __init__
    self.results = batch()
                   ^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in __call__
    return [func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in <listcomp>
    return [func(*args, **kwargs)
            ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 127, in __call__
    return self.function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 141, in _parallel_build_estimators
    estimator_fit(X_, y, sample_weight=curr_sample_weight)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1169, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  warnings.warn(some_fits_failed_message, FitFailedWarning)
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:979: UserWarning: One or more of the test scores are non-finite: [        nan -0.43098534         nan -0.43123255]
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

{'estimator__penalty': 'l2'}
-0.43098533602002503

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


# Define the parameter grid
params = {'base_estimator__penalty': ['l1', 'l2', 'elasticnet', None]}

# Create GridSearchCV instance
gcv = GridSearchCV(bagg, param_grid=params, cv=kfold, scoring='neg_log_loss')

# Fit the model
gcv.fit(X, y)

# Print the best parameters and best score
print(gcv.best_params_)
print(gcv.best_score_)

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

KeyboardInterrupt


# Define the parameter grid
params = {'base_estimator__penalty': ['l1', 'l2', 'elasticnet', None]}

# Create GridSearchCV instance
gcv = GridSearchCV(bagg, param_grid=params, cv=kfold, scoring='neg_log_loss')

# Fit the model
gcv.fit(X, y)

# Print the best parameters and best score
print(gcv.best_params_)
print(gcv.best_score_)

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  estimator = estimator.set_params(**clone(parameters, safe=False))
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:425: FitFailedWarning: 
10 fits failed out of a total of 20.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 729, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 338, in fit
    return self._fit(X, y, self.max_samples, sample_weight=sample_weight)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 473, in _fit
    all_results = Parallel(
                  ^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 65, in __call__
    return super().__call__(iterable_with_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 1085, in __call__
    if self.dispatch_one_batch(iterator):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 901, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 819, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
             ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 597, in __init__
    self.results = batch()
                   ^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in __call__
    return [func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in <listcomp>
    return [func(*args, **kwargs)
            ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 127, in __call__
    return self.function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 141, in _parallel_build_estimators
    estimator_fit(X_, y, sample_weight=curr_sample_weight)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1169, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 729, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 338, in fit
    return self._fit(X, y, self.max_samples, sample_weight=sample_weight)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 473, in _fit
    all_results = Parallel(
                  ^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 65, in __call__
    return super().__call__(iterable_with_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 1085, in __call__
    if self.dispatch_one_batch(iterator):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 901, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 819, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async
    result = ImmediateResult(func)
             ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\_parallel_backends.py", line 597, in __init__
    self.results = batch()
                   ^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in __call__
    return [func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\joblib\parallel.py", line 288, in <listcomp>
    return [func(*args, **kwargs)
            ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\parallel.py", line 127, in __call__
    return self.function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_bagging.py", line 141, in _parallel_build_estimators
    estimator_fit(X_, y, sample_weight=curr_sample_weight)
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1169, in fit
    solver = _check_solver(self.solver, self.penalty, self.dual)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py", line 56, in _check_solver
    raise ValueError(
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.

  warnings.warn(some_fits_failed_message, FitFailedWarning)
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:979: UserWarning: One or more of the test scores are non-finite: [        nan -0.43098534         nan -0.43123255]
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:930: FutureWarning: Parameter 'base_estimator' of BaggingClassifier is deprecated in favor of 'estimator'. See BaggingClassifier's docstring for more details.
  self.best_estimator_ = clone(base_estimator).set_params(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

{'base_estimator__penalty': 'l2'}
-0.43098533602002503

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

0.8017337186041342
0.43427987931266165
{'base_estimator': 'deprecated', 'bootstrap': True, 'bootstrap_features': False, 'estimator__C': 1.0, 'estimator__class_weight': None, 'estimator__dual': False, 'estimator__fit_intercept': True, 'estimator__intercept_scaling': 1, 'estimator__l1_ratio': None, 'estimator__max_iter': 100, 'estimator__multi_class': 'auto', 'estimator__n_jobs': None, 'estimator__penalty': 'l2', 'estimator__random_state': None, 'estimator__solver': 'lbfgs', 'estimator__tol': 0.0001, 'estimator__verbose': 0, 'estimator__warm_start': False, 'estimator': LogisticRegression(), 'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 15, 'n_jobs': None, 'oob_score': False, 'random_state': 23, 'verbose': 0, 'warm_start': False}

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[5], line 47
     44 params = {'min_samples_split':[2,5,20,80,100],'max_depth':[3,4,6,7,None],'min_samples_leaf':[1,5,10,20]}
     45 gcv = GridSearchCV(bagg, param_grid=params,
     46                    cv=kfold, scoring='neg_log_loss')
---> 47 gcv.fit(X, y)
     48 print(gcv.best_params_)
     49 print(gcv.best_score_)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1422, in GridSearchCV._run_search(self, evaluate_candidates)
   1420 def _run_search(self, evaluate_candidates):
   1421     """Search all candidates in param_grid"""
-> 1422     evaluate_candidates(ParameterGrid(self.param_grid))

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:845, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    837 if self.verbose > 0:
    838     print(
    839         "Fitting {0} folds for each of {1} candidates,"
    840         " totalling {2} fits".format(
    841             n_splits, n_candidates, n_candidates * n_splits
    842         )
    843     )
--> 845 out = parallel(
    846     delayed(_fit_and_score)(
    847         clone(base_estimator),
    848         X,
    849         y,
    850         train=train,
    851         test=test,
    852         parameters=parameters,
    853         split_progress=(split_idx, n_splits),
    854         candidate_progress=(cand_idx, n_candidates),
    855         **fit_and_score_kwargs,
    856     )
    857     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    858         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    859     )
    860 )
    862 if len(out) < 1:
    863     raise ValueError(
    864         "No fits were performed. "
    865         "Was the CV iterator empty? "
    866         "Were there no candidates?"
    867     )

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1085, in Parallel.__call__(self, iterable)
   1076 try:
   1077     # Only set self._iterating to True if at least a batch
   1078     # was dispatched. In particular this covers the edge
   (...)
   1082     # was very quick and its callback already dispatched all the
   1083     # remaining jobs.
   1084     self._iterating = False
-> 1085     if self.dispatch_one_batch(iterator):
   1086         self._iterating = self._original_iterator is not None
   1088     while self.dispatch_one_batch(iterator):

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    710 fit_params = _check_fit_params(X, fit_params, train)
    712 if parameters is not None:
    713     # here we clone the parameters, since sometimes the parameters
    714     # themselves might be estimators, e.g. when we search over different
    715     # estimators in a pipeline.
    716     # ref: https://github.com/scikit-learn/scikit-learn/pull/26786
--> 717     estimator = estimator.set_params(**clone(parameters, safe=False))
    719 start_time = time.time()
    721 X_train, y_train = _safe_split(estimator, X, y, train)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:230, in BaseEstimator.set_params(self, **params)
    228 if key not in valid_params:
    229     local_valid_params = self._get_param_names()
--> 230     raise ValueError(
    231         f"Invalid parameter {key!r} for estimator {self}. "
    232         f"Valid parameters are: {local_valid_params!r}."
    233     )
    235 if delim:
    236     nested_params[key][sub_key] = value

ValueError: Invalid parameter 'max_depth' for estimator BaggingClassifier(estimator=LogisticRegression(), n_estimators=15,
                  random_state=23). Valid parameters are: ['base_estimator', 'bootstrap', 'bootstrap_features', 'estimator', 'max_features', 'max_samples', 'n_estimators', 'n_jobs', 'oob_score', 'random_state', 'verbose', 'warm_start'].

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Accuracy: 0.8017337186041342
Log Loss: 0.43427987931266165

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[6], line 44
     40 params = {'base_estimator__min_samples_split': [2, 5, 20, 80, 100],
     41           'base_estimator__max_depth': [3, 4, 6, 7, None],
     42           'base_estimator__min_samples_leaf': [1, 5, 10, 20]}
     43 gcv = GridSearchCV(bagg, param_grid=params, cv=kfold, scoring='neg_log_loss')
---> 44 gcv.fit(X, y)
     46 # Print the best parameters and best score
     47 print("Best Parameters:", gcv.best_params_)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1422, in GridSearchCV._run_search(self, evaluate_candidates)
   1420 def _run_search(self, evaluate_candidates):
   1421     """Search all candidates in param_grid"""
-> 1422     evaluate_candidates(ParameterGrid(self.param_grid))

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:845, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    837 if self.verbose > 0:
    838     print(
    839         "Fitting {0} folds for each of {1} candidates,"
    840         " totalling {2} fits".format(
    841             n_splits, n_candidates, n_candidates * n_splits
    842         )
    843     )
--> 845 out = parallel(
    846     delayed(_fit_and_score)(
    847         clone(base_estimator),
    848         X,
    849         y,
    850         train=train,
    851         test=test,
    852         parameters=parameters,
    853         split_progress=(split_idx, n_splits),
    854         candidate_progress=(cand_idx, n_candidates),
    855         **fit_and_score_kwargs,
    856     )
    857     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    858         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    859     )
    860 )
    862 if len(out) < 1:
    863     raise ValueError(
    864         "No fits were performed. "
    865         "Was the CV iterator empty? "
    866         "Were there no candidates?"
    867     )

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1085, in Parallel.__call__(self, iterable)
   1076 try:
   1077     # Only set self._iterating to True if at least a batch
   1078     # was dispatched. In particular this covers the edge
   (...)
   1082     # was very quick and its callback already dispatched all the
   1083     # remaining jobs.
   1084     self._iterating = False
-> 1085     if self.dispatch_one_batch(iterator):
   1086         self._iterating = self._original_iterator is not None
   1088     while self.dispatch_one_batch(iterator):

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    710 fit_params = _check_fit_params(X, fit_params, train)
    712 if parameters is not None:
    713     # here we clone the parameters, since sometimes the parameters
    714     # themselves might be estimators, e.g. when we search over different
    715     # estimators in a pipeline.
    716     # ref: https://github.com/scikit-learn/scikit-learn/pull/26786
--> 717     estimator = estimator.set_params(**clone(parameters, safe=False))
    719 start_time = time.time()
    721 X_train, y_train = _safe_split(estimator, X, y, train)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:263, in BaseEstimator.set_params(self, **params)
    253         warnings.warn(
    254             (
    255                 f"Parameter 'base_estimator' of {self.__class__.__name__} is"
   (...)
    260             stacklevel=2,
    261         )
    262         key = "estimator"
--> 263     valid_params[key].set_params(**sub_params)
    265 return self

File ~\anaconda3\Lib\site-packages\sklearn\base.py:230, in BaseEstimator.set_params(self, **params)
    228 if key not in valid_params:
    229     local_valid_params = self._get_param_names()
--> 230     raise ValueError(
    231         f"Invalid parameter {key!r} for estimator {self}. "
    232         f"Valid parameters are: {local_valid_params!r}."
    233     )
    235 if delim:
    236     nested_params[key][sub_key] = value

ValueError: Invalid parameter 'max_depth' for estimator LogisticRegression(). Valid parameters are: ['C', 'class_weight', 'dual', 'fit_intercept', 'intercept_scaling', 'l1_ratio', 'max_iter', 'multi_class', 'n_jobs', 'penalty', 'random_state', 'solver', 'tol', 'verbose', 'warm_start'].


bagg = BaggingClassifier(base_estimator=lr, n_estimators=15,
                         random_state=23)

params = {'base_estimator__min_samples_split': [2, 5, 20, 80, 100],
          'base_estimator__max_depth': [3, 4, 6, 7, None],
          'base_estimator__min_samples_leaf': [1, 5, 10, 20]}
gcv = GridSearchCV(bagg, param_grid=params, cv=kfold, scoring='neg_log_loss')
gcv.fit(X, y)

# Print the best parameters and best score
print("Best Parameters:", gcv.best_params_)
print("Best Log Loss:", gcv.best_score_)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[10], line 8
      4 params = {'base_estimator__min_samples_split': [2, 5, 20, 80, 100],
      5           'base_estimator__max_depth': [3, 4, 6, 7, None],
      6           'base_estimator__min_samples_leaf': [1, 5, 10, 20]}
      7 gcv = GridSearchCV(bagg, param_grid=params, cv=kfold, scoring='neg_log_loss')
----> 8 gcv.fit(X, y)
     10 # Print the best parameters and best score
     11 print("Best Parameters:", gcv.best_params_)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1422, in GridSearchCV._run_search(self, evaluate_candidates)
   1420 def _run_search(self, evaluate_candidates):
   1421     """Search all candidates in param_grid"""
-> 1422     evaluate_candidates(ParameterGrid(self.param_grid))

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:845, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    837 if self.verbose > 0:
    838     print(
    839         "Fitting {0} folds for each of {1} candidates,"
    840         " totalling {2} fits".format(
    841             n_splits, n_candidates, n_candidates * n_splits
    842         )
    843     )
--> 845 out = parallel(
    846     delayed(_fit_and_score)(
    847         clone(base_estimator),
    848         X,
    849         y,
    850         train=train,
    851         test=test,
    852         parameters=parameters,
    853         split_progress=(split_idx, n_splits),
    854         candidate_progress=(cand_idx, n_candidates),
    855         **fit_and_score_kwargs,
    856     )
    857     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    858         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    859     )
    860 )
    862 if len(out) < 1:
    863     raise ValueError(
    864         "No fits were performed. "
    865         "Was the CV iterator empty? "
    866         "Were there no candidates?"
    867     )

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1085, in Parallel.__call__(self, iterable)
   1076 try:
   1077     # Only set self._iterating to True if at least a batch
   1078     # was dispatched. In particular this covers the edge
   (...)
   1082     # was very quick and its callback already dispatched all the
   1083     # remaining jobs.
   1084     self._iterating = False
-> 1085     if self.dispatch_one_batch(iterator):
   1086         self._iterating = self._original_iterator is not None
   1088     while self.dispatch_one_batch(iterator):

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    710 fit_params = _check_fit_params(X, fit_params, train)
    712 if parameters is not None:
    713     # here we clone the parameters, since sometimes the parameters
    714     # themselves might be estimators, e.g. when we search over different
    715     # estimators in a pipeline.
    716     # ref: https://github.com/scikit-learn/scikit-learn/pull/26786
--> 717     estimator = estimator.set_params(**clone(parameters, safe=False))
    719 start_time = time.time()
    721 X_train, y_train = _safe_split(estimator, X, y, train)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:263, in BaseEstimator.set_params(self, **params)
    253         warnings.warn(
    254             (
    255                 f"Parameter 'base_estimator' of {self.__class__.__name__} is"
   (...)
    260             stacklevel=2,
    261         )
    262         key = "estimator"
--> 263     valid_params[key].set_params(**sub_params)
    265 return self

File ~\anaconda3\Lib\site-packages\sklearn\base.py:230, in BaseEstimator.set_params(self, **params)
    228 if key not in valid_params:
    229     local_valid_params = self._get_param_names()
--> 230     raise ValueError(
    231         f"Invalid parameter {key!r} for estimator {self}. "
    232         f"Valid parameters are: {local_valid_params!r}."
    233     )
    235 if delim:
    236     nested_params[key][sub_key] = value

ValueError: Invalid parameter 'max_depth' for estimator LogisticRegression(). Valid parameters are: ['C', 'class_weight', 'dual', 'fit_intercept', 'intercept_scaling', 'l1_ratio', 'max_iter', 'multi_class', 'n_jobs', 'penalty', 'random_state', 'solver', 'tol', 'verbose', 'warm_start'].


import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GridSearchCV

hr=pd.read_csv('HR_comma_sep.csv')
dum_hr=pd.get_dummies(hr,drop_first=True)
x=dum_hr.drop('left',axis=1)
y=dum_hr['left']

rf=RandomForestClassifier(random_state=23,n_estimators=20)
params={'max_features':[2,4,6,8,10],'min_samples_split':[2,5,10,20,60,80,100],'max_depth': [3, 4, 5, 6, 7, None],'min_samples_leaf':[1,5,10,20]}


# Define StratifiedKFold
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=23)

# Create GridSearchCV instance
gcv = GridSearchCV(rf, param_grid=params, cv=kfold, scoring='neg_log_loss')

# Fit the model
gcv.fit(x, y)

# Print the best parameters and best log loss score
print("Best Parameters:", gcv.best_params_)
print("Best Log Loss:", gcv.best_score_)  #

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[22], line 8
      5 gcv = GridSearchCV(rf, param_grid=params, cv=kfold, scoring='neg_log_loss')
      7 # Fit the model
----> 8 gcv.fit(x, y)
     10 # Print the best parameters and best log loss score
     11 print("Best Parameters:", gcv.best_params_)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1422, in GridSearchCV._run_search(self, evaluate_candidates)
   1420 def _run_search(self, evaluate_candidates):
   1421     """Search all candidates in param_grid"""
-> 1422     evaluate_candidates(ParameterGrid(self.param_grid))

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:845, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    837 if self.verbose > 0:
    838     print(
    839         "Fitting {0} folds for each of {1} candidates,"
    840         " totalling {2} fits".format(
    841             n_splits, n_candidates, n_candidates * n_splits
    842         )
    843     )
--> 845 out = parallel(
    846     delayed(_fit_and_score)(
    847         clone(base_estimator),
    848         X,
    849         y,
    850         train=train,
    851         test=test,
    852         parameters=parameters,
    853         split_progress=(split_idx, n_splits),
    854         candidate_progress=(cand_idx, n_candidates),
    855         **fit_and_score_kwargs,
    856     )
    857     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    858         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    859     )
    860 )
    862 if len(out) < 1:
    863     raise ValueError(
    864         "No fits were performed. "
    865         "Was the CV iterator empty? "
    866         "Were there no candidates?"
    867     )

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1088, in Parallel.__call__(self, iterable)
   1085 if self.dispatch_one_batch(iterator):
   1086     self._iterating = self._original_iterator is not None
-> 1088 while self.dispatch_one_batch(iterator):
   1089     pass
   1091 if pre_dispatch == "all" or n_jobs == 1:
   1092     # The iterable was consumed all at once by the above for loop.
   1093     # No need to wait for async callbacks to trigger to
   1094     # consumption.

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:729, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    727         estimator.fit(X_train, **fit_params)
    728     else:
--> 729         estimator.fit(X_train, y_train, **fit_params)
    731 except Exception:
    732     # Note fit time as time until error
    733     fit_time = time.time() - start_time

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\ensemble\_forest.py:456, in BaseForest.fit(self, X, y, sample_weight)
    445 trees = [
    446     self._make_estimator(append=False, random_state=random_state)
    447     for i in range(n_more_estimators)
    448 ]
    450 # Parallel loop: we prefer the threading backend as the Cython code
    451 # for fitting the trees is internally releasing the Python GIL
    452 # making threading more efficient than multiprocessing in
    453 # that case. However, for joblib 0.12+ we respect any
    454 # parallel_backend contexts set at a higher level,
    455 # since correctness does not rely on using threads.
--> 456 trees = Parallel(
    457     n_jobs=self.n_jobs,
    458     verbose=self.verbose,
    459     prefer="threads",
    460 )(
    461     delayed(_parallel_build_trees)(
    462         t,
    463         self.bootstrap,
    464         X,
    465         y,
    466         sample_weight,
    467         i,
    468         len(trees),
    469         verbose=self.verbose,
    470         class_weight=self.class_weight,
    471         n_samples_bootstrap=n_samples_bootstrap,
    472     )
    473     for i, t in enumerate(trees)
    474 )
    476 # Collect newly grown trees
    477 self.estimators_.extend(trees)

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1088, in Parallel.__call__(self, iterable)
   1085 if self.dispatch_one_batch(iterator):
   1086     self._iterating = self._original_iterator is not None
-> 1088 while self.dispatch_one_batch(iterator):
   1089     pass
   1091 if pre_dispatch == "all" or n_jobs == 1:
   1092     # The iterable was consumed all at once by the above for loop.
   1093     # No need to wait for async callbacks to trigger to
   1094     # consumption.

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\ensemble\_forest.py:188, in _parallel_build_trees(tree, bootstrap, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)
    185     elif class_weight == "balanced_subsample":
    186         curr_sample_weight *= compute_sample_weight("balanced", y, indices=indices)
--> 188     tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
    189 else:
    190     tree.fit(X, y, sample_weight=sample_weight, check_input=False)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\tree\_classes.py:959, in DecisionTreeClassifier.fit(self, X, y, sample_weight, check_input)
    928 @_fit_context(prefer_skip_nested_validation=True)
    929 def fit(self, X, y, sample_weight=None, check_input=True):
    930     """Build a decision tree classifier from the training set (X, y).
    931 
    932     Parameters
   (...)
    956         Fitted estimator.
    957     """
--> 959     super()._fit(
    960         X,
    961         y,
    962         sample_weight=sample_weight,
    963         check_input=check_input,
    964     )
    965     return self

File ~\anaconda3\Lib\site-packages\sklearn\tree\_classes.py:443, in BaseDecisionTree._fit(self, X, y, sample_weight, check_input, missing_values_in_feature_mask)
    432 else:
    433     builder = BestFirstTreeBuilder(
    434         splitter,
    435         min_samples_split,
   (...)
    440         self.min_impurity_decrease,
    441     )
--> 443 builder.build(self.tree_, X, y, sample_weight, missing_values_in_feature_mask)
    445 if self.n_outputs_ == 1 and is_classifier(self):
    446     self.n_classes_ = self.n_classes_[0]

KeyboardInterrupt:


import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GridSearchCV

hr=pd.read_csv('HR_comma_sep.csv')
dum_hr=pd.get_dummies(hr,drop_first=True)
x=dum_hr.drop('left',axis=1)
y=dum_hr['left']

rf=RandomForestClassifier(random_state=23)
params={'min_samples_split':[2,5,10,20,60,80,100],'max_depth': [3, 4, 5, 6, 7, None],'min_samples_leaf':[1,5,10,20],n_estimators=20}

#for individual
# Define StratifiedKFold
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=23)

# Create GridSearchCV instance
gcv = GridSearchCV(rf, param_grid=params, cv=kfold, scoring='neg_log_loss')

# Fit the model
gcv.fit(x, y)

# Print the best parameters and best log loss score
print("Best Parameters:", gcv.best_params_)
print("Best Log Loss:", gcv.best_score_)  #

  Cell In[23], line 13
    params={'min_samples_split':[2,5,10,20,60,80,100],'max_depth': [3, 4, 5, 6, 7, None],'min_samples_leaf':[1,5,10,20],n_estimators=20}
                                                                                                                                   ^
SyntaxError: ':' expected after dictionary key


#importance tree(Features importance)

print(bm_tree.feature_importances)
df_imp=pd.DataFrame({'Features':list(x.columns),'Importance':bm_tree.feature_importance_})
df_imp=df.imp[df_imp['Importance']>0]
plr.barhh(df_imp['Features'].df_imp['Importance'])
plt.title('Tree')
plt.show()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[24], line 3
      1 #importance tree(Features importance)
----> 3 print(bm_tree.feature_importances)
      4 df_imp=pd.DataFrame({'Features':list(x.columns),'Importance':bm_tree.feature_importance_})
      5 df_imp=df.imp[df_imp['Importance']>0]

NameError: name 'bm_tree' is not defined


import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GridSearchCV

hr=pd.read_csv('HR_comma_sep.csv')
dum_hr=pd.get_dummies(hr,drop_first=True)
x=dum_hr.drop('left',axis=1)
y=dum_hr['left']

rf=RandomForestClassifier(random_state=23,n_estimators=20)
params={'max_features':[2,4,6,8,10],'min_samples_split':[2,5,10,20,60,80,100],'max_depth': [3, 4, 5, 6, 7, None],'min_samples_leaf':[1,5,10,20]}

# Define StratifiedKFold
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=23)

# Create GridSearchCV instance
gcv = GridSearchCV(rf, param_grid=params, cv=kfold, scoring='neg_log_loss')

# Fit the model
gcv.fit(x, y)

# Print the best parameters and best log loss score
print("Best Parameters:", gcv.best_params_)
print("Best Log Loss:", gcv.best_score_)  #


#importance tree(Features importance)

# Plotting bar chart for feature importance
plt.barh(df_imp['Features'], df_imp['Importance'])
plt.title('Random Forest Feature Importance')
plt.xlabel('Importance')
plt.show()

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[25], line 22
     19 gcv = GridSearchCV(rf, param_grid=params, cv=kfold, scoring='neg_log_loss')
     21 # Fit the model
---> 22 gcv.fit(x, y)
     24 # Print the best parameters and best log loss score
     25 print("Best Parameters:", gcv.best_params_)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1422, in GridSearchCV._run_search(self, evaluate_candidates)
   1420 def _run_search(self, evaluate_candidates):
   1421     """Search all candidates in param_grid"""
-> 1422     evaluate_candidates(ParameterGrid(self.param_grid))

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:845, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    837 if self.verbose > 0:
    838     print(
    839         "Fitting {0} folds for each of {1} candidates,"
    840         " totalling {2} fits".format(
    841             n_splits, n_candidates, n_candidates * n_splits
    842         )
    843     )
--> 845 out = parallel(
    846     delayed(_fit_and_score)(
    847         clone(base_estimator),
    848         X,
    849         y,
    850         train=train,
    851         test=test,
    852         parameters=parameters,
    853         split_progress=(split_idx, n_splits),
    854         candidate_progress=(cand_idx, n_candidates),
    855         **fit_and_score_kwargs,
    856     )
    857     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    858         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    859     )
    860 )
    862 if len(out) < 1:
    863     raise ValueError(
    864         "No fits were performed. "
    865         "Was the CV iterator empty? "
    866         "Were there no candidates?"
    867     )

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1088, in Parallel.__call__(self, iterable)
   1085 if self.dispatch_one_batch(iterator):
   1086     self._iterating = self._original_iterator is not None
-> 1088 while self.dispatch_one_batch(iterator):
   1089     pass
   1091 if pre_dispatch == "all" or n_jobs == 1:
   1092     # The iterable was consumed all at once by the above for loop.
   1093     # No need to wait for async callbacks to trigger to
   1094     # consumption.

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:729, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    727         estimator.fit(X_train, **fit_params)
    728     else:
--> 729         estimator.fit(X_train, y_train, **fit_params)
    731 except Exception:
    732     # Note fit time as time until error
    733     fit_time = time.time() - start_time

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\ensemble\_forest.py:456, in BaseForest.fit(self, X, y, sample_weight)
    445 trees = [
    446     self._make_estimator(append=False, random_state=random_state)
    447     for i in range(n_more_estimators)
    448 ]
    450 # Parallel loop: we prefer the threading backend as the Cython code
    451 # for fitting the trees is internally releasing the Python GIL
    452 # making threading more efficient than multiprocessing in
    453 # that case. However, for joblib 0.12+ we respect any
    454 # parallel_backend contexts set at a higher level,
    455 # since correctness does not rely on using threads.
--> 456 trees = Parallel(
    457     n_jobs=self.n_jobs,
    458     verbose=self.verbose,
    459     prefer="threads",
    460 )(
    461     delayed(_parallel_build_trees)(
    462         t,
    463         self.bootstrap,
    464         X,
    465         y,
    466         sample_weight,
    467         i,
    468         len(trees),
    469         verbose=self.verbose,
    470         class_weight=self.class_weight,
    471         n_samples_bootstrap=n_samples_bootstrap,
    472     )
    473     for i, t in enumerate(trees)
    474 )
    476 # Collect newly grown trees
    477 self.estimators_.extend(trees)

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1088, in Parallel.__call__(self, iterable)
   1085 if self.dispatch_one_batch(iterator):
   1086     self._iterating = self._original_iterator is not None
-> 1088 while self.dispatch_one_batch(iterator):
   1089     pass
   1091 if pre_dispatch == "all" or n_jobs == 1:
   1092     # The iterable was consumed all at once by the above for loop.
   1093     # No need to wait for async callbacks to trigger to
   1094     # consumption.

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\ensemble\_forest.py:188, in _parallel_build_trees(tree, bootstrap, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)
    185     elif class_weight == "balanced_subsample":
    186         curr_sample_weight *= compute_sample_weight("balanced", y, indices=indices)
--> 188     tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
    189 else:
    190     tree.fit(X, y, sample_weight=sample_weight, check_input=False)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\tree\_classes.py:959, in DecisionTreeClassifier.fit(self, X, y, sample_weight, check_input)
    928 @_fit_context(prefer_skip_nested_validation=True)
    929 def fit(self, X, y, sample_weight=None, check_input=True):
    930     """Build a decision tree classifier from the training set (X, y).
    931 
    932     Parameters
   (...)
    956         Fitted estimator.
    957     """
--> 959     super()._fit(
    960         X,
    961         y,
    962         sample_weight=sample_weight,
    963         check_input=check_input,
    964     )
    965     return self

File ~\anaconda3\Lib\site-packages\sklearn\tree\_classes.py:443, in BaseDecisionTree._fit(self, X, y, sample_weight, check_input, missing_values_in_feature_mask)
    432 else:
    433     builder = BestFirstTreeBuilder(
    434         splitter,
    435         min_samples_split,
   (...)
    440         self.min_impurity_decrease,
    441     )
--> 443 builder.build(self.tree_, X, y, sample_weight, missing_values_in_feature_mask)
    445 if self.n_outputs_ == 1 and is_classifier(self):
    446     self.n_classes_ = self.n_classes_[0]

KeyboardInterrupt:


# Create RandomForestClassifier with OOB scoring
rf = RandomForestClassifier(random_state=23, n_estimators=20, oob_score=True)

# Fit the model
rf.fit(X_train, y_train)

# Access the OOB score
oob_score = rf.oob_score_

# Make predictions on the test set
y_pred = rf.predict(X_test)

# Print the OOB score
print("OOB Score:", oob_score)

# You can also print other evaluation metrics
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

OOB Score: 0.9858041158536586
Accuracy: 0.9882196043565237

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\ensemble\_forest.py:578: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable OOB estimates.
  warn(


y_pred=rf.predict(X_test)
print(accuracy_score(y_test,y_pred))

0.9882196043565237


#4. Consider the dataset Datasets/Housing.csv with y=price, and all other variables as features, Try the following parameters on that data with random forest regressor:
#a. Min_samples_split: 2,3,4,5,6,10, 20
#b. Min_samples_leaf: 1,2,3,4,5,6,10, 20
#c. Max_depth: None, 2,3,4,5,6,7\

import os
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score

# Load data
os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Datasets")
hs = pd.read_csv("Housing.csv")

# Identify categorical columns
categorical_cols = hs.select_dtypes(include=['object']).columns

# One-hot encode categorical columns
hs_encoded = pd.get_dummies(hs, columns=categorical_cols, drop_first=True)

# Separate features and target
X = hs_encoded.drop('price', axis=1)
y = hs_encoded['price']

# Create RandomForestRegressor with improved parameters
rf = RandomForestRegressor(random_state=23,
                            n_estimators=100,  # Increase the number of trees
                            max_depth=None,  # Allow trees to grow deeper
                            min_samples_split=2,  # Adjust the minimum samples for split
                            min_samples_leaf=1)  # Adjust the minimum samples for leaf

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=23)

# Fit the model
rf.fit(X_train, y_train)

# Predict on the test set
y_pred = rf.predict(X_test)

# Calculate mean absolute error
mae = mean_absolute_error(y_test, y_pred)
print("Mean Absolute Error:", mae)

mse=mean_squared_error(y_test,y_pred)
print('Mean squared error:',mse)

r2=r2_score(y_test,y_pred)
print('R2 score:',r2)

Mean Absolute Error: 11118.617479674798
Mean squared error: 234186764.47166666
R2 score: 0.636824499032288


# Define StratifiedKld
from sklearn.model_selection import GridSearchCV,KFold
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=23)

# Create GridSearchCV instance
gcv = GridSearchCV(rf, param_grid=params, cv=kfold, scoring='neg_log_loss')

# Fit the model
gcv.fit(X, y)

# Print the best parameters and best log loss score
print("Best Parameters:", gcv.best_params_)
impotprint("Best Log Loss:", gcv.best_score_)  #

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_split.py:737: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.
  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: RandomForestRegressor should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[44], line 9
      6 gcv = GridSearchCV(rf, param_grid=params, cv=kfold, scoring='neg_log_loss')
      8 # Fit the model
----> 9 gcv.fit(X, y)
     11 # Print the best parameters and best log loss score
     12 print("Best Parameters:", gcv.best_params_)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1422, in GridSearchCV._run_search(self, evaluate_candidates)
   1420 def _run_search(self, evaluate_candidates):
   1421     """Search all candidates in param_grid"""
-> 1422     evaluate_candidates(ParameterGrid(self.param_grid))

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:845, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    837 if self.verbose > 0:
    838     print(
    839         "Fitting {0} folds for each of {1} candidates,"
    840         " totalling {2} fits".format(
    841             n_splits, n_candidates, n_candidates * n_splits
    842         )
    843     )
--> 845 out = parallel(
    846     delayed(_fit_and_score)(
    847         clone(base_estimator),
    848         X,
    849         y,
    850         train=train,
    851         test=test,
    852         parameters=parameters,
    853         split_progress=(split_idx, n_splits),
    854         candidate_progress=(cand_idx, n_candidates),
    855         **fit_and_score_kwargs,
    856     )
    857     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    858         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    859     )
    860 )
    862 if len(out) < 1:
    863     raise ValueError(
    864         "No fits were performed. "
    865         "Was the CV iterator empty? "
    866         "Were there no candidates?"
    867     )

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1088, in Parallel.__call__(self, iterable)
   1085 if self.dispatch_one_batch(iterator):
   1086     self._iterating = self._original_iterator is not None
-> 1088 while self.dispatch_one_batch(iterator):
   1089     pass
   1091 if pre_dispatch == "all" or n_jobs == 1:
   1092     # The iterable was consumed all at once by the above for loop.
   1093     # No need to wait for async callbacks to trigger to
   1094     # consumption.

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:729, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    727         estimator.fit(X_train, **fit_params)
    728     else:
--> 729         estimator.fit(X_train, y_train, **fit_params)
    731 except Exception:
    732     # Note fit time as time until error
    733     fit_time = time.time() - start_time

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\ensemble\_forest.py:456, in BaseForest.fit(self, X, y, sample_weight)
    445 trees = [
    446     self._make_estimator(append=False, random_state=random_state)
    447     for i in range(n_more_estimators)
    448 ]
    450 # Parallel loop: we prefer the threading backend as the Cython code
    451 # for fitting the trees is internally releasing the Python GIL
    452 # making threading more efficient than multiprocessing in
    453 # that case. However, for joblib 0.12+ we respect any
    454 # parallel_backend contexts set at a higher level,
    455 # since correctness does not rely on using threads.
--> 456 trees = Parallel(
    457     n_jobs=self.n_jobs,
    458     verbose=self.verbose,
    459     prefer="threads",
    460 )(
    461     delayed(_parallel_build_trees)(
    462         t,
    463         self.bootstrap,
    464         X,
    465         y,
    466         sample_weight,
    467         i,
    468         len(trees),
    469         verbose=self.verbose,
    470         class_weight=self.class_weight,
    471         n_samples_bootstrap=n_samples_bootstrap,
    472     )
    473     for i, t in enumerate(trees)
    474 )
    476 # Collect newly grown trees
    477 self.estimators_.extend(trees)

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1088, in Parallel.__call__(self, iterable)
   1085 if self.dispatch_one_batch(iterator):
   1086     self._iterating = self._original_iterator is not None
-> 1088 while self.dispatch_one_batch(iterator):
   1089     pass
   1091 if pre_dispatch == "all" or n_jobs == 1:
   1092     # The iterable was consumed all at once by the above for loop.
   1093     # No need to wait for async callbacks to trigger to
   1094     # consumption.

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\ensemble\_forest.py:188, in _parallel_build_trees(tree, bootstrap, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)
    185     elif class_weight == "balanced_subsample":
    186         curr_sample_weight *= compute_sample_weight("balanced", y, indices=indices)
--> 188     tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
    189 else:
    190     tree.fit(X, y, sample_weight=sample_weight, check_input=False)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\tree\_classes.py:1320, in DecisionTreeRegressor.fit(self, X, y, sample_weight, check_input)
   1290 @_fit_context(prefer_skip_nested_validation=True)
   1291 def fit(self, X, y, sample_weight=None, check_input=True):
   1292     """Build a decision tree regressor from the training set (X, y).
   1293 
   1294     Parameters
   (...)
   1317         Fitted estimator.
   1318     """
-> 1320     super()._fit(
   1321         X,
   1322         y,
   1323         sample_weight=sample_weight,
   1324         check_input=check_input,
   1325     )
   1326     return self

File ~\anaconda3\Lib\site-packages\sklearn\tree\_classes.py:443, in BaseDecisionTree._fit(self, X, y, sample_weight, check_input, missing_values_in_feature_mask)
    432 else:
    433     builder = BestFirstTreeBuilder(
    434         splitter,
    435         min_samples_split,
   (...)
    440         self.min_impurity_decrease,
    441     )
--> 443 builder.build(self.tree_, X, y, sample_weight, missing_values_in_feature_mask)
    445 if self.n_outputs_ == 1 and is_classifier(self):
    446     self.n_classes_ = self.n_classes_[0]

KeyboardInterrupt:


import pandas as pd
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV,StratifiedKFold
import matplotlib.pyplot as plt
import os
os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\human-resources-analytics')

# Load the dataset
hr = pd.read_csv('HR_comma_sep.csv')

# Create dummy variables for categorical features
dum_hr = pd.get_dummies(hr, drop_first=True)

# Separate features (x) and target variable (y)
X = dum_hr.drop('left', axis=1)
y = dum_hr['left']


ada=AdaBoostClassifier(random_state=23)
kfold=StratifiedKFold(n_splits=5,shuffle=True,random_state=23)

params={'n_estimators':[25,50,75,100]}
gcv=GridSearchCV(ada,param_grid=params,cv=kfold,verbose=3,scoring='neg_log_loss')

gcv.fit(X,y)
print(gcv.best_params_)
print(gcv.best_score_)

Fitting 5 folds for each of 4 candidates, totalling 20 fits
[CV 1/5] END ..................n_estimators=25;, score=-0.520 total time=   0.2s
[CV 2/5] END ..................n_estimators=25;, score=-0.525 total time=   0.2s
[CV 3/5] END ..................n_estimators=25;, score=-0.526 total time=   0.4s
[CV 4/5] END ..................n_estimators=25;, score=-0.530 total time=   0.4s
[CV 5/5] END ..................n_estimators=25;, score=-0.518 total time=   0.4s
[CV 1/5] END ..................n_estimators=50;, score=-0.584 total time=   0.8s
[CV 2/5] END ..................n_estimators=50;, score=-0.585 total time=   0.8s
[CV 3/5] END ..................n_estimators=50;, score=-0.587 total time=   0.8s
[CV 4/5] END ..................n_estimators=50;, score=-0.588 total time=   0.8s
[CV 5/5] END ..................n_estimators=50;, score=-0.582 total time=   0.8s
[CV 1/5] END ..................n_estimators=75;, score=-0.614 total time=   1.2s
[CV 2/5] END ..................n_estimators=75;, score=-0.616 total time=   1.2s
[CV 3/5] END ..................n_estimators=75;, score=-0.616 total time=   1.2s
[CV 4/5] END ..................n_estimators=75;, score=-0.617 total time=   1.2s
[CV 5/5] END ..................n_estimators=75;, score=-0.613 total time=   1.2s
[CV 1/5] END .................n_estimators=100;, score=-0.632 total time=   1.4s
[CV 2/5] END .................n_estimators=100;, score=-0.632 total time=   0.9s
[CV 3/5] END .................n_estimators=100;, score=-0.633 total time=   1.0s
[CV 4/5] END .................n_estimators=100;, score=-0.634 total time=   1.7s
[CV 5/5] END .................n_estimators=100;, score=-0.631 total time=   1.7s
{'n_estimators': 25}
-0.523713098264005


from sklearn.ensemble import AdaBoostRegressor
from sklearn.model_selection import GridSearchCV,KFold

import os
os.chdir('C:\\Users\\Amansh\\Downloads\\PML\Datasets')

# Load the dataset
df = pd.read_csv('Boston.csv')

# Create dummy variables for categorical features
dum_df = pd.get_dummies(df, drop_first=True)

# Separate features (x) and target variable (y)
X = dum_df.drop('medv', axis=1)
y = dum_df['medv']

ada=AdaBoostRegressor(random_state=23)
kfold=KFold(n_splits=5,shuffle=True,random_state=23)

params={'n_estimators':[25,50,75,100]}
gcv=GridSearchCV(ada,param_grid=params,cv=kfold,verbose=3)

gcv.fit(X,y)
print('best_paramsn: ',gcv.best_params_)
print('best score :',gcv.best_score_)

Fitting 5 folds for each of 4 candidates, totalling 20 fits
[CV 1/5] END ...................n_estimators=25;, score=0.774 total time=   0.0s
[CV 2/5] END ...................n_estimators=25;, score=0.806 total time=   0.1s
[CV 3/5] END ...................n_estimators=25;, score=0.887 total time=   0.1s
[CV 4/5] END ...................n_estimators=25;, score=0.774 total time=   0.1s
[CV 5/5] END ...................n_estimators=25;, score=0.873 total time=   0.1s
[CV 1/5] END ...................n_estimators=50;, score=0.782 total time=   0.1s
[CV 2/5] END ...................n_estimators=50;, score=0.829 total time=   0.1s
[CV 3/5] END ...................n_estimators=50;, score=0.884 total time=   0.1s
[CV 4/5] END ...................n_estimators=50;, score=0.767 total time=   0.1s
[CV 5/5] END ...................n_estimators=50;, score=0.870 total time=   0.1s
[CV 1/5] END ...................n_estimators=75;, score=0.793 total time=   0.2s
[CV 2/5] END ...................n_estimators=75;, score=0.837 total time=   0.1s
[CV 3/5] END ...................n_estimators=75;, score=0.886 total time=   0.2s
[CV 4/5] END ...................n_estimators=75;, score=0.758 total time=   0.2s
[CV 5/5] END ...................n_estimators=75;, score=0.867 total time=   0.2s
[CV 1/5] END ..................n_estimators=100;, score=0.797 total time=   0.4s
[CV 2/5] END ..................n_estimators=100;, score=0.833 total time=   0.4s
[CV 3/5] END ..................n_estimators=100;, score=0.881 total time=   0.5s
[CV 4/5] END ..................n_estimators=100;, score=0.750 total time=   0.5s
[CV 5/5] END ..................n_estimators=100;, score=0.870 total time=   0.4s
best_paramsn:  {'n_estimators': 75}
best score : 0.8283098718084337


import numpy as np
import pandas as pd
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV,StratifiedKFold
import matplotlib.pyplot as plt
import os
os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\human-resources-analytics')

# Load the dataset
hr = pd.read_csv('HR_comma_sep.csv')

# Create dummy variables for categorical features
dum_hr = pd.get_dummies(hr, drop_first=True)

# Separate features (x) and target variable (y)
X = dum_hr.drop('left', axis=1)
y = dum_hr['left']

gbm=GradientBoostingClassifier(random_state=23)
kfold=StratifiedKFold(n_splits=5,shuffle=True,random_state=23)

params={'n_estimators':[50,75,100],'learning_rate':np.linspace(0.001,0.7,5),'max_depth':[None,2,3,5]}
gcv=GridSearchCV(gbm,param_grid=params,cv=kfold,verbose=3,scoring='neg_log_loss')

gcv.fit(X,y)
print(gcv.best_params_)
print(gcv.best_score_)

Fitting 5 folds for each of 60 candidates, totalling 300 fits
[CV 1/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=-0.505 total time=   7.0s
[CV 2/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=-0.505 total time=   5.3s
[CV 3/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=-0.505 total time=   7.1s
[CV 4/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=-0.505 total time=   6.6s
[CV 5/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=-0.505 total time=   5.8s
[CV 1/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=-0.486 total time=  10.0s
[CV 2/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=-0.486 total time=   9.6s
[CV 3/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=-0.485 total time=   8.9s
[CV 4/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=-0.485 total time=  11.0s
[CV 5/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=-0.485 total time=   8.4s
[CV 1/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=-0.468 total time=  12.4s
[CV 2/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=-0.468 total time=  13.4s
[CV 3/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=-0.467 total time=  13.5s
[CV 4/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=-0.467 total time=  12.7s
[CV 5/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=-0.467 total time=  12.8s
[CV 1/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=-0.526 total time=   0.8s
[CV 2/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=-0.526 total time=   0.8s
[CV 3/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=-0.526 total time=   0.8s
[CV 4/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=-0.526 total time=   0.8s
[CV 5/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=-0.526 total time=   0.7s
[CV 1/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=-0.516 total time=   0.7s
[CV 2/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=-0.515 total time=   1.3s
[CV 3/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=-0.515 total time=   1.1s
[CV 4/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=-0.516 total time=   1.0s
[CV 5/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=-0.516 total time=   0.7s
[CV 1/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=-0.506 total time=   1.2s
[CV 2/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=-0.505 total time=   1.6s
[CV 3/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=-0.506 total time=   1.7s
[CV 4/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=-0.506 total time=   1.7s
[CV 5/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=-0.506 total time=   1.7s
[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-0.513 total time=   1.2s
[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-0.512 total time=   1.2s
[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-0.513 total time=   1.2s
[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-0.513 total time=   1.4s
[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-0.512 total time=   0.7s
[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=-0.497 total time=   0.9s
[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=-0.496 total time=   1.4s
[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=-0.497 total time=   1.8s
[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=-0.497 total time=   1.8s
[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=-0.496 total time=   1.8s
[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-0.482 total time=   2.4s
[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-0.481 total time=   2.6s
[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-0.482 total time=   2.2s
[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-0.483 total time=   1.4s
[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-0.480 total time=   2.2s
[CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-0.509 total time=   2.0s
[CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-0.509 total time=   1.9s
[CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-0.508 total time=   2.0s
[CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-0.509 total time=   2.0s
[CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-0.508 total time=   2.0s
[CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=-0.491 total time=   2.4s
[CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=-0.491 total time=   2.3s
[CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=-0.490 total time=   3.0s
[CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=-0.491 total time=   3.0s
[CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=-0.490 total time=   3.0s
[CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-0.475 total time=   3.6s
[CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-0.474 total time=   2.6s
[CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-0.473 total time=   4.0s
[CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-0.474 total time=   4.0s
[CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-0.474 total time=   4.0s
[CV 1/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=-0.190 total time=   4.7s
[CV 2/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=-0.160 total time=   6.6s
[CV 3/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=-0.141 total time=   5.8s
[CV 4/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=-0.142 total time=   6.0s
[CV 5/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=-0.149 total time=   6.4s
[CV 1/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=-0.275 total time=   8.0s
[CV 2/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=-0.234 total time=   9.9s
[CV 3/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=-0.204 total time=   8.1s
[CV 4/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=-0.205 total time=   9.2s
[CV 5/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=-0.216 total time=   8.6s
[CV 1/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=-0.328 total time=  10.7s
[CV 2/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=-0.276 total time=  12.0s
[CV 3/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=-0.239 total time=  11.6s
[CV 4/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=-0.241 total time=  11.0s
[CV 5/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=-0.261 total time=  10.4s
[CV 1/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=-0.147 total time=   0.7s
[CV 2/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=-0.138 total time=   0.8s
[CV 3/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=-0.133 total time=   0.8s
[CV 4/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=-0.143 total time=   0.8s
[CV 5/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=-0.140 total time=   0.8s
[CV 1/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=-0.124 total time=   1.3s
[CV 2/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=-0.114 total time=   1.2s
[CV 3/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=-0.108 total time=   1.3s
[CV 4/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=-0.118 total time=   1.2s
[CV 5/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=-0.115 total time=   1.2s
[CV 1/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=-0.113 total time=   1.7s
[CV 2/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=-0.102 total time=   1.3s
[CV 3/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=-0.096 total time=   0.8s
[CV 4/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=-0.107 total time=   1.0s
[CV 5/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=-0.102 total time=   1.7s
[CV 1/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=-0.104 total time=   1.2s
[CV 2/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=-0.095 total time=   1.2s
[CV 3/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=-0.089 total time=   1.2s
[CV 4/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=-0.097 total time=   1.2s
[CV 5/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=-0.091 total time=   1.1s
[CV 1/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=-0.095 total time=   1.8s
[CV 2/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=-0.086 total time=   1.8s
[CV 3/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=-0.079 total time=   1.7s
[CV 4/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=-0.086 total time=   1.0s
[CV 5/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=-0.084 total time=   0.9s
[CV 1/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=-0.089 total time=   2.5s
[CV 2/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=-0.080 total time=   2.4s
[CV 3/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=-0.075 total time=   2.4s
[CV 4/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=-0.080 total time=   2.4s
[CV 5/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=-0.078 total time=   2.4s
[CV 1/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=-0.075 total time=   1.6s
[CV 2/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=-0.076 total time=   1.1s
[CV 3/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=-0.060 total time=   1.6s
[CV 4/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=-0.066 total time=   2.0s
[CV 5/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=-0.073 total time=   2.0s
[CV 1/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=-0.072 total time=   3.1s
[CV 2/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=-0.075 total time=   3.0s
[CV 3/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=-0.053 total time=   2.5s
[CV 4/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=-0.059 total time=   1.9s
[CV 5/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=-0.068 total time=   3.1s
[CV 1/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=-0.064 total time=   4.1s
[CV 2/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=-0.070 total time=   4.2s
[CV 3/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=-0.051 total time=   3.3s
[CV 4/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=-0.053 total time=   3.7s
[CV 5/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=-0.070 total time=   4.1s
[CV 1/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=-0.333 total time=   6.3s
[CV 2/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=-0.267 total time=   4.4s
[CV 3/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=-0.239 total time=   6.4s
[CV 4/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=-0.243 total time=   5.7s
[CV 5/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=-0.261 total time=   4.9s
[CV 1/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=-0.332 total time=   6.5s
[CV 2/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=-0.265 total time=   5.3s
[CV 3/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=-0.236 total time=   6.3s
[CV 4/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=-0.242 total time=   6.8s
[CV 5/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=-0.262 total time=   4.5s
[CV 1/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=-0.332 total time=   6.7s
[CV 2/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=-0.265 total time=   6.1s
[CV 3/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=-0.236 total time=   5.6s
[CV 4/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=-0.242 total time=   7.0s
[CV 5/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=-0.262 total time=   4.6s
[CV 1/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=-0.111 total time=   0.8s
[CV 2/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=-0.097 total time=   0.8s
[CV 3/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=-0.092 total time=   0.8s
[CV 4/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=-0.100 total time=   0.8s
[CV 5/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=-0.098 total time=   0.8s
[CV 1/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=-0.102 total time=   1.2s
[CV 2/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=-0.089 total time=   1.3s
[CV 3/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=-0.083 total time=   1.2s
[CV 4/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=-0.091 total time=   1.2s
[CV 5/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=-0.090 total time=   1.2s
[CV 1/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=-0.098 total time=   1.7s
[CV 2/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=-0.084 total time=   1.2s
[CV 3/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=-0.077 total time=   1.0s
[CV 4/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=-0.087 total time=   1.3s
[CV 5/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=-0.085 total time=   1.6s
[CV 1/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=-0.092 total time=   1.2s
[CV 2/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=-0.080 total time=   1.2s
[CV 3/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=-0.073 total time=   1.2s
[CV 4/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=-0.080 total time=   1.2s
[CV 5/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=-0.079 total time=   1.2s
[CV 1/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=-0.087 total time=   1.8s
[CV 2/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=-0.078 total time=   1.8s
[CV 3/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=-0.066 total time=   1.4s
[CV 4/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=-0.073 total time=   1.1s
[CV 5/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=-0.074 total time=   1.2s
[CV 1/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=-0.084 total time=   2.5s
[CV 2/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=-0.073 total time=   2.4s
[CV 3/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=-0.063 total time=   2.5s
[CV 4/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=-0.081 total time=   2.4s
[CV 5/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=-0.073 total time=   2.4s
[CV 1/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=-0.077 total time=   1.2s
[CV 2/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=-0.066 total time=   1.0s
[CV 3/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=-0.053 total time=   2.0s
[CV 4/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=-0.058 total time=   2.0s
[CV 5/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=-0.076 total time=   2.0s
[CV 1/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=-0.072 total time=   3.1s
[CV 2/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=-0.061 total time=   3.1s
[CV 3/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=-0.050 total time=   2.2s
[CV 4/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=-0.053 total time=   2.2s
[CV 5/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=-0.076 total time=   3.0s
[CV 1/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=-0.067 total time=   4.0s
[CV 2/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=-0.060 total time=   4.2s
[CV 3/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=-0.048 total time=   2.7s
[CV 4/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=-0.050 total time=   3.8s
[CV 5/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=-0.077 total time=   4.2s
[CV 1/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=-0.326 total time=   4.5s
[CV 2/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=-0.270 total time=   3.0s
[CV 3/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=-0.242 total time=   4.0s
[CV 4/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=-0.238 total time=   4.5s
[CV 5/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=-0.262 total time=   4.2s
[CV 1/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=-0.326 total time=   3.2s
[CV 2/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=-0.270 total time=   4.2s
[CV 3/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=-0.242 total time=   4.5s
[CV 4/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=-0.238 total time=   4.5s
[CV 5/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=-0.262 total time=   2.6s
[CV 1/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=-0.326 total time=   4.5s
[CV 2/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=-0.270 total time=   4.5s
[CV 3/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=-0.242 total time=   4.6s
[CV 4/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=-0.238 total time=   2.8s
[CV 5/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=-0.262 total time=   4.5s
[CV 1/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=-0.101 total time=   0.8s
[CV 2/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=-0.090 total time=   0.8s
[CV 3/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=-0.081 total time=   0.8s
[CV 4/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=-0.093 total time=   0.8s
[CV 5/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=-0.088 total time=   0.8s
[CV 1/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=-0.094 total time=   1.3s
[CV 2/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=-0.086 total time=   1.2s
[CV 3/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=-0.077 total time=   1.2s
[CV 4/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=-0.086 total time=   0.6s
[CV 5/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=-0.083 total time=   0.6s
[CV 1/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=-0.091 total time=   1.1s
[CV 2/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=-0.084 total time=   1.7s
[CV 3/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=-0.075 total time=   1.5s
[CV 4/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=-0.079 total time=   1.7s
[CV 5/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=-0.081 total time=   1.6s
[CV 1/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=-0.085 total time=   1.0s
[CV 2/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=-0.088 total time=   1.2s
[CV 3/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=-0.073 total time=   1.2s
[CV 4/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=-0.074 total time=   1.3s
[CV 5/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=-0.072 total time=   1.3s
[CV 1/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=-0.080 total time=   1.0s
[CV 2/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=-0.169 total time=   0.9s
[CV 3/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=-0.069 total time=   1.6s
[CV 4/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=-0.068 total time=   1.8s
[CV 5/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=-0.069 total time=   1.8s
[CV 1/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=-0.079 total time=   2.4s
[CV 2/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=-0.077 total time=   2.5s
[CV 3/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=-0.068 total time=   2.3s
[CV 4/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=-0.064 total time=   1.7s
[CV 5/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=-0.082 total time=   1.4s
[CV 1/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=-0.063 total time=   2.1s
[CV 2/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=-0.078 total time=   2.0s
[CV 3/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=-0.105 total time=   2.0s
[CV 4/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=-0.052 total time=   2.0s
[CV 5/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=-0.073 total time=   2.1s
[CV 1/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=-0.060 total time=   2.9s
[CV 2/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=-0.073 total time=   1.9s
[CV 3/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=-0.104 total time=   2.7s
[CV 4/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=-0.049 total time=   3.1s
[CV 5/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=-0.073 total time=   2.9s
[CV 1/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=-0.065 total time=   4.2s
[CV 2/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=-0.075 total time=   2.4s
[CV 3/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=-0.103 total time=   4.0s
[CV 4/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=-0.052 total time=   4.1s
[CV 5/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=-0.075 total time=   4.1s

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[19], line 26
     23 params={'n_estimators':[50,75,100],'learning_rate':np.linspace(0.001,0.7,5),'max_depth':[None,2,3,5]}
     24 gcv=GridSearchCV(gbm,param_grid=params,cv=kfold,verbose=3,scoring='neg_log_loss')
---> 26 gcv.fit(X,y)
     27 print(gcv.best_params_)
     28 print(gcv.best_score_)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1422, in GridSearchCV._run_search(self, evaluate_candidates)
   1420 def _run_search(self, evaluate_candidates):
   1421     """Search all candidates in param_grid"""
-> 1422     evaluate_candidates(ParameterGrid(self.param_grid))

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:845, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    837 if self.verbose > 0:
    838     print(
    839         "Fitting {0} folds for each of {1} candidates,"
    840         " totalling {2} fits".format(
    841             n_splits, n_candidates, n_candidates * n_splits
    842         )
    843     )
--> 845 out = parallel(
    846     delayed(_fit_and_score)(
    847         clone(base_estimator),
    848         X,
    849         y,
    850         train=train,
    851         test=test,
    852         parameters=parameters,
    853         split_progress=(split_idx, n_splits),
    854         candidate_progress=(cand_idx, n_candidates),
    855         **fit_and_score_kwargs,
    856     )
    857     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    858         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    859     )
    860 )
    862 if len(out) < 1:
    863     raise ValueError(
    864         "No fits were performed. "
    865         "Was the CV iterator empty? "
    866         "Were there no candidates?"
    867     )

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1088, in Parallel.__call__(self, iterable)
   1085 if self.dispatch_one_batch(iterator):
   1086     self._iterating = self._original_iterator is not None
-> 1088 while self.dispatch_one_batch(iterator):
   1089     pass
   1091 if pre_dispatch == "all" or n_jobs == 1:
   1092     # The iterable was consumed all at once by the above for loop.
   1093     # No need to wait for async callbacks to trigger to
   1094     # consumption.

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:729, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    727         estimator.fit(X_train, **fit_params)
    728     else:
--> 729         estimator.fit(X_train, y_train, **fit_params)
    731 except Exception:
    732     # Note fit time as time until error
    733     fit_time = time.time() - start_time

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\ensemble\_gb.py:532, in BaseGradientBoosting.fit(self, X, y, sample_weight, monitor)
    529     self._resize_state()
    531 # fit the boosting stages
--> 532 n_stages = self._fit_stages(
    533     X_train,
    534     y_train,
    535     raw_predictions,
    536     sample_weight_train,
    537     self._rng,
    538     X_val,
    539     y_val,
    540     sample_weight_val,
    541     begin_at_stage,
    542     monitor,
    543 )
    545 # change shape of arrays after fit (early-stopping or additional ests)
    546 if n_stages != self.estimators_.shape[0]:

File ~\anaconda3\Lib\site-packages\sklearn\ensemble\_gb.py:610, in BaseGradientBoosting._fit_stages(self, X, y, raw_predictions, sample_weight, random_state, X_val, y_val, sample_weight_val, begin_at_stage, monitor)
    603         initial_loss = loss_(
    604             y[~sample_mask],
    605             raw_predictions[~sample_mask],
    606             sample_weight[~sample_mask],
    607         )
    609 # fit next stage of trees
--> 610 raw_predictions = self._fit_stage(
    611     i,
    612     X,
    613     y,
    614     raw_predictions,
    615     sample_weight,
    616     sample_mask,
    617     random_state,
    618     X_csc,
    619     X_csr,
    620 )
    622 # track loss
    623 if do_oob:

File ~\anaconda3\Lib\site-packages\sklearn\ensemble\_gb.py:245, in BaseGradientBoosting._fit_stage(self, i, X, y, raw_predictions, sample_weight, sample_mask, random_state, X_csc, X_csr)
    242     sample_weight = sample_weight * sample_mask.astype(np.float64)
    244 X = X_csr if X_csr is not None else X
--> 245 tree.fit(X, residual, sample_weight=sample_weight, check_input=False)
    247 # update tree leaves
    248 loss.update_terminal_regions(
    249     tree.tree_,
    250     X,
   (...)
    257     k=k,
    258 )

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\tree\_classes.py:1320, in DecisionTreeRegressor.fit(self, X, y, sample_weight, check_input)
   1290 @_fit_context(prefer_skip_nested_validation=True)
   1291 def fit(self, X, y, sample_weight=None, check_input=True):
   1292     """Build a decision tree regressor from the training set (X, y).
   1293 
   1294     Parameters
   (...)
   1317         Fitted estimator.
   1318     """
-> 1320     super()._fit(
   1321         X,
   1322         y,
   1323         sample_weight=sample_weight,
   1324         check_input=check_input,
   1325     )
   1326     return self

File ~\anaconda3\Lib\site-packages\sklearn\tree\_classes.py:443, in BaseDecisionTree._fit(self, X, y, sample_weight, check_input, missing_values_in_feature_mask)
    432 else:
    433     builder = BestFirstTreeBuilder(
    434         splitter,
    435         min_samples_split,
   (...)
    440         self.min_impurity_decrease,
    441     )
--> 443 builder.build(self.tree_, X, y, sample_weight, missing_values_in_feature_mask)
    445 if self.n_outputs_ == 1 and is_classifier(self):
    446     self.n_classes_ = self.n_classes_[0]

KeyboardInterrupt:


import numpy as np
import pandas as pd
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV,KFold
import matplotlib.pyplot as plt
import os
os.chdir('C:\\Users\\Amansh\\Downloads\\PML\Datasets')

# Load the dataset
df = pd.read_csv('Boston.csv')

# Create dummy variables for categorical features
dum_df = pd.get_dummies(df, drop_first=True)

# Separate features (x) and target variable (y)
X = dum_df.drop('medv', axis=1)
y = dum_df['medv']

gbm=GradientBoostingRegressor(random_state=23)
kfold=KFold(n_splits=5,shuffle=True,random_state=23)

params={'n_estimators':[50,75,100],'learning_rate':np.linspace(0.001,0.7,5),'max_depth':[None,2,3,5]}
gcv=GridSearchCV(gbm,param_grid=params,cv=kfold,verbose=3)

gcv.fit(X,y)
print(gcv.best_params_)
print(gcv.best_score_)

Fitting 5 folds for each of 60 candidates, totalling 300 fits
[CV 1/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=0.086 total time=   0.6s
[CV 2/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=0.077 total time=   0.6s
[CV 3/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=0.084 total time=   0.6s
[CV 4/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=0.047 total time=   0.6s
[CV 5/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=0.069 total time=   0.6s
[CV 1/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=0.126 total time=   1.1s
[CV 2/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=0.115 total time=   1.0s
[CV 3/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=0.124 total time=   1.0s
[CV 4/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=0.079 total time=   1.0s
[CV 5/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=0.107 total time=   0.9s
[CV 1/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=0.164 total time=   1.3s
[CV 2/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=0.151 total time=   1.3s
[CV 3/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=0.162 total time=   1.4s
[CV 4/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=0.110 total time=   1.3s
[CV 5/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=0.143 total time=   1.3s
[CV 1/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=0.070 total time=   0.0s
[CV 2/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=0.062 total time=   0.0s
[CV 3/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=0.049 total time=   0.0s
[CV 4/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=0.033 total time=   0.0s
[CV 5/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=0.048 total time=   0.0s
[CV 1/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=0.103 total time=   0.1s
[CV 2/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=0.094 total time=   0.1s
[CV 3/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=0.073 total time=   0.1s
[CV 4/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=0.058 total time=   0.1s
[CV 5/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=0.077 total time=   0.1s
[CV 1/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=0.134 total time=   0.1s
[CV 2/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=0.125 total time=   0.1s
[CV 3/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=0.098 total time=   0.1s
[CV 4/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=0.082 total time=   0.1s
[CV 5/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=0.105 total time=   0.2s
[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=0.078 total time=   0.1s
[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=0.066 total time=   0.1s
[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=0.057 total time=   0.1s
[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=0.036 total time=   0.1s
[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=0.060 total time=   0.1s
[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=0.114 total time=   0.3s
[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=0.100 total time=   0.2s
[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=0.085 total time=   0.2s
[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=0.063 total time=   0.2s
[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=0.094 total time=   0.2s
[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.148 total time=   0.3s
[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.133 total time=   0.2s
[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.112 total time=   0.2s
[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.088 total time=   0.2s
[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=0.127 total time=   0.2s
[CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=0.088 total time=   0.2s
[CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=0.073 total time=   0.2s
[CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=0.079 total time=   0.2s
[CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=0.047 total time=   0.2s
[CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=0.066 total time=   0.2s
[CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=0.130 total time=   0.3s
[CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=0.108 total time=   0.3s
[CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=0.116 total time=   0.3s
[CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=0.079 total time=   0.3s
[CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=0.103 total time=   0.3s
[CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=0.169 total time=   0.5s
[CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=0.141 total time=   0.5s
[CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=0.151 total time=   0.5s
[CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=0.109 total time=   0.5s
[CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=0.138 total time=   0.5s
[CV 1/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=0.750 total time=   0.6s
[CV 2/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=0.828 total time=   0.6s
[CV 3/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=0.881 total time=   0.6s
[CV 4/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=0.679 total time=   0.6s
[CV 5/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=0.823 total time=   0.6s
[CV 1/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=0.750 total time=   1.0s
[CV 2/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=0.828 total time=   1.0s
[CV 3/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=0.881 total time=   1.2s
[CV 4/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=0.679 total time=   1.1s
[CV 5/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=0.823 total time=   0.9s
[CV 1/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=0.750 total time=   1.3s
[CV 2/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=0.828 total time=   1.2s
[CV 3/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=0.881 total time=   1.3s
[CV 4/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=0.679 total time=   1.3s
[CV 5/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=0.823 total time=   1.2s
[CV 1/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=0.840 total time=   0.0s
[CV 2/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=0.826 total time=   0.0s
[CV 3/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=0.877 total time=   0.0s
[CV 4/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=0.818 total time=   0.0s
[CV 5/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=0.898 total time=   0.0s
[CV 1/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=0.832 total time=   0.1s
[CV 2/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=0.834 total time=   0.1s
[CV 3/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=0.885 total time=   0.1s
[CV 4/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=0.828 total time=   0.1s
[CV 5/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=0.904 total time=   0.1s
[CV 1/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=0.832 total time=   0.1s
[CV 2/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=0.837 total time=   0.1s
[CV 3/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=0.890 total time=   0.1s
[CV 4/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=0.828 total time=   0.1s
[CV 5/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=0.912 total time=   0.1s
[CV 1/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=0.835 total time=   0.1s
[CV 2/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=0.906 total time=   0.1s
[CV 3/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=0.901 total time=   0.1s
[CV 4/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=0.912 total time=   0.1s
[CV 5/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=0.917 total time=   0.1s
[CV 1/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=0.832 total time=   0.2s
[CV 2/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=0.909 total time=   0.2s
[CV 3/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=0.901 total time=   0.2s
[CV 4/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=0.916 total time=   0.2s
[CV 5/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=0.922 total time=   0.2s
[CV 1/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=0.829 total time=   0.3s
[CV 2/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=0.910 total time=   0.3s
[CV 3/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=0.901 total time=   0.3s
[CV 4/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=0.918 total time=   0.3s
[CV 5/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=0.925 total time=   0.3s
[CV 1/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=0.848 total time=   0.2s
[CV 2/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=0.899 total time=   0.5s
[CV 3/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=0.927 total time=   0.6s
[CV 4/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=0.846 total time=   0.2s
[CV 5/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=0.892 total time=   0.2s
[CV 1/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=0.849 total time=   0.3s
[CV 2/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=0.901 total time=   0.3s
[CV 3/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=0.927 total time=   0.3s
[CV 4/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=0.849 total time=   0.3s
[CV 5/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=0.893 total time=   0.3s
[CV 1/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=0.850 total time=   0.4s
[CV 2/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=0.901 total time=   0.4s
[CV 3/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=0.927 total time=   0.5s
[CV 4/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=0.849 total time=   0.5s
[CV 5/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=0.894 total time=   0.4s
[CV 1/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=0.751 total time=   0.5s
[CV 2/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=0.820 total time=   0.5s
[CV 3/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=0.881 total time=   0.6s
[CV 4/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=0.668 total time=   0.5s
[CV 5/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=0.828 total time=   0.5s
[CV 1/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=0.751 total time=   0.5s
[CV 2/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=0.820 total time=   0.5s
[CV 3/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=0.881 total time=   0.6s
[CV 4/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=0.668 total time=   0.6s
[CV 5/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=0.828 total time=   0.5s
[CV 1/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=0.751 total time=   0.6s
[CV 2/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=0.820 total time=   0.6s
[CV 3/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=0.881 total time=   0.7s
[CV 4/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=0.668 total time=   0.6s
[CV 5/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=0.828 total time=   0.6s
[CV 1/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=0.817 total time=   0.0s
[CV 2/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=0.805 total time=   0.0s
[CV 3/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=0.867 total time=   0.0s
[CV 4/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=0.795 total time=   0.0s
[CV 5/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=0.894 total time=   0.0s
[CV 1/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=0.819 total time=   0.1s
[CV 2/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=0.810 total time=   0.1s
[CV 3/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=0.869 total time=   0.1s
[CV 4/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=0.800 total time=   0.1s
[CV 5/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=0.898 total time=   0.1s
[CV 1/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=0.811 total time=   0.1s
[CV 2/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=0.805 total time=   0.1s
[CV 3/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=0.865 total time=   0.1s
[CV 4/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=0.805 total time=   0.1s
[CV 5/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=0.894 total time=   0.1s
[CV 1/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=0.791 total time=   0.1s
[CV 2/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=0.891 total time=   0.1s
[CV 3/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=0.881 total time=   0.1s
[CV 4/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=0.887 total time=   0.1s
[CV 5/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=0.923 total time=   0.1s
[CV 1/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=0.797 total time=   0.2s
[CV 2/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=0.891 total time=   0.2s
[CV 3/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=0.877 total time=   0.1s
[CV 4/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=0.887 total time=   0.2s
[CV 5/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=0.925 total time=   0.2s
[CV 1/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=0.793 total time=   0.2s
[CV 2/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=0.887 total time=   0.2s
[CV 3/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=0.879 total time=   0.2s
[CV 4/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=0.888 total time=   0.2s
[CV 5/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=0.926 total time=   0.2s
[CV 1/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=0.843 total time=   0.2s
[CV 2/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=0.868 total time=   0.2s
[CV 3/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=0.924 total time=   0.2s
[CV 4/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=0.875 total time=   0.2s
[CV 5/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=0.896 total time=   0.2s
[CV 1/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=0.843 total time=   0.4s
[CV 2/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=0.869 total time=   0.3s
[CV 3/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=0.924 total time=   0.3s
[CV 4/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=0.877 total time=   0.3s
[CV 5/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=0.896 total time=   0.3s
[CV 1/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=0.843 total time=   0.5s
[CV 2/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=0.869 total time=   0.5s
[CV 3/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=0.924 total time=   0.4s
[CV 4/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=0.876 total time=   0.6s
[CV 5/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=0.896 total time=   0.7s
[CV 1/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=0.747 total time=   0.4s
[CV 2/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=0.830 total time=   0.6s
[CV 3/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=0.878 total time=   0.5s
[CV 4/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=0.677 total time=   0.6s
[CV 5/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=0.800 total time=   0.8s
[CV 1/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=0.747 total time=   0.6s
[CV 2/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=0.830 total time=   0.6s
[CV 3/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=0.878 total time=   0.6s
[CV 4/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=0.677 total time=   0.6s
[CV 5/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=0.800 total time=   0.5s
[CV 1/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=0.747 total time=   0.5s
[CV 2/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=0.830 total time=   0.6s
[CV 3/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=0.878 total time=   0.6s
[CV 4/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=0.677 total time=   0.5s
[CV 5/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=0.800 total time=   0.6s
[CV 1/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=0.807 total time=   0.1s
[CV 2/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=0.837 total time=   0.1s
[CV 3/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=0.890 total time=   0.1s
[CV 4/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=0.817 total time=   0.1s
[CV 5/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=0.894 total time=   0.1s
[CV 1/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=0.806 total time=   0.2s
[CV 2/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=0.824 total time=   0.2s
[CV 3/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=0.891 total time=   0.1s
[CV 4/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=0.825 total time=   0.2s
[CV 5/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=0.899 total time=   0.1s
[CV 1/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=0.806 total time=   0.3s
[CV 2/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=0.829 total time=   0.3s
[CV 3/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=0.888 total time=   0.2s
[CV 4/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=0.833 total time=   0.2s
[CV 5/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=0.898 total time=   0.2s
[CV 1/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=0.813 total time=   0.1s
[CV 2/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=0.867 total time=   0.1s
[CV 3/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=0.862 total time=   0.1s
[CV 4/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=0.842 total time=   0.1s
[CV 5/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=0.896 total time=   0.1s
[CV 1/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=0.812 total time=   0.2s
[CV 2/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=0.870 total time=   0.2s
[CV 3/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=0.853 total time=   0.2s
[CV 4/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=0.838 total time=   0.2s
[CV 5/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=0.897 total time=   0.2s
[CV 1/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=0.812 total time=   0.4s
[CV 2/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=0.868 total time=   0.3s
[CV 3/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=0.853 total time=   0.3s
[CV 4/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=0.837 total time=   0.4s
[CV 5/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=0.896 total time=   0.3s
[CV 1/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=0.818 total time=   0.2s
[CV 2/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=0.890 total time=   0.3s
[CV 3/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=0.915 total time=   0.3s
[CV 4/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=0.869 total time=   0.2s
[CV 5/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=0.865 total time=   0.2s
[CV 1/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=0.818 total time=   0.3s
[CV 2/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=0.890 total time=   0.3s
[CV 3/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=0.915 total time=   0.3s
[CV 4/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=0.869 total time=   0.3s
[CV 5/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=0.865 total time=   0.4s
[CV 1/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=0.818 total time=   0.5s
[CV 2/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=0.890 total time=   0.4s
[CV 3/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=0.915 total time=   0.5s
[CV 4/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=0.869 total time=   0.5s
[CV 5/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=0.865 total time=   0.5s
[CV 1/5] END learning_rate=0.7, max_depth=None, n_estimators=50;, score=0.744 total time=   0.2s
[CV 2/5] END learning_rate=0.7, max_depth=None, n_estimators=50;, score=0.821 total time=   0.2s
[CV 3/5] END learning_rate=0.7, max_depth=None, n_estimators=50;, score=0.878 total time=   0.3s
[CV 4/5] END learning_rate=0.7, max_depth=None, n_estimators=50;, score=0.664 total time=   0.5s
[CV 5/5] END learning_rate=0.7, max_depth=None, n_estimators=50;, score=0.813 total time=   0.5s
[CV 1/5] END learning_rate=0.7, max_depth=None, n_estimators=75;, score=0.744 total time=   0.6s
[CV 2/5] END learning_rate=0.7, max_depth=None, n_estimators=75;, score=0.821 total time=   0.6s
[CV 3/5] END learning_rate=0.7, max_depth=None, n_estimators=75;, score=0.878 total time=   0.7s
[CV 4/5] END learning_rate=0.7, max_depth=None, n_estimators=75;, score=0.664 total time=   0.5s
[CV 5/5] END learning_rate=0.7, max_depth=None, n_estimators=75;, score=0.813 total time=   0.5s
[CV 1/5] END learning_rate=0.7, max_depth=None, n_estimators=100;, score=0.744 total time=   0.5s
[CV 2/5] END learning_rate=0.7, max_depth=None, n_estimators=100;, score=0.821 total time=   0.5s
[CV 3/5] END learning_rate=0.7, max_depth=None, n_estimators=100;, score=0.878 total time=   0.3s
[CV 4/5] END learning_rate=0.7, max_depth=None, n_estimators=100;, score=0.664 total time=   0.3s
[CV 5/5] END learning_rate=0.7, max_depth=None, n_estimators=100;, score=0.813 total time=   0.3s
[CV 1/5] END learning_rate=0.7, max_depth=2, n_estimators=50;, score=0.739 total time=   0.1s
[CV 2/5] END learning_rate=0.7, max_depth=2, n_estimators=50;, score=0.845 total time=   0.1s
[CV 3/5] END learning_rate=0.7, max_depth=2, n_estimators=50;, score=0.865 total time=   0.1s
[CV 4/5] END learning_rate=0.7, max_depth=2, n_estimators=50;, score=0.791 total time=   0.1s
[CV 5/5] END learning_rate=0.7, max_depth=2, n_estimators=50;, score=0.858 total time=   0.1s
[CV 1/5] END learning_rate=0.7, max_depth=2, n_estimators=75;, score=0.730 total time=   0.2s
[CV 2/5] END learning_rate=0.7, max_depth=2, n_estimators=75;, score=0.845 total time=   0.2s
[CV 3/5] END learning_rate=0.7, max_depth=2, n_estimators=75;, score=0.864 total time=   0.3s
[CV 4/5] END learning_rate=0.7, max_depth=2, n_estimators=75;, score=0.793 total time=   0.3s
[CV 5/5] END learning_rate=0.7, max_depth=2, n_estimators=75;, score=0.862 total time=   0.3s
[CV 1/5] END learning_rate=0.7, max_depth=2, n_estimators=100;, score=0.723 total time=   0.4s
[CV 2/5] END learning_rate=0.7, max_depth=2, n_estimators=100;, score=0.852 total time=   0.2s
[CV 3/5] END learning_rate=0.7, max_depth=2, n_estimators=100;, score=0.862 total time=   0.3s
[CV 4/5] END learning_rate=0.7, max_depth=2, n_estimators=100;, score=0.804 total time=   0.2s
[CV 5/5] END learning_rate=0.7, max_depth=2, n_estimators=100;, score=0.853 total time=   0.2s
[CV 1/5] END learning_rate=0.7, max_depth=3, n_estimators=50;, score=0.775 total time=   0.1s
[CV 2/5] END learning_rate=0.7, max_depth=3, n_estimators=50;, score=0.840 total time=   0.1s
[CV 3/5] END learning_rate=0.7, max_depth=3, n_estimators=50;, score=0.895 total time=   0.1s
[CV 4/5] END learning_rate=0.7, max_depth=3, n_estimators=50;, score=0.828 total time=   0.1s
[CV 5/5] END learning_rate=0.7, max_depth=3, n_estimators=50;, score=0.904 total time=   0.1s
[CV 1/5] END learning_rate=0.7, max_depth=3, n_estimators=75;, score=0.777 total time=   0.2s
[CV 2/5] END learning_rate=0.7, max_depth=3, n_estimators=75;, score=0.837 total time=   0.2s
[CV 3/5] END learning_rate=0.7, max_depth=3, n_estimators=75;, score=0.891 total time=   0.2s
[CV 4/5] END learning_rate=0.7, max_depth=3, n_estimators=75;, score=0.830 total time=   0.2s
[CV 5/5] END learning_rate=0.7, max_depth=3, n_estimators=75;, score=0.902 total time=   0.2s
[CV 1/5] END learning_rate=0.7, max_depth=3, n_estimators=100;, score=0.776 total time=   0.2s
[CV 2/5] END learning_rate=0.7, max_depth=3, n_estimators=100;, score=0.837 total time=   0.2s
[CV 3/5] END learning_rate=0.7, max_depth=3, n_estimators=100;, score=0.890 total time=   0.2s
[CV 4/5] END learning_rate=0.7, max_depth=3, n_estimators=100;, score=0.827 total time=   0.2s
[CV 5/5] END learning_rate=0.7, max_depth=3, n_estimators=100;, score=0.901 total time=   0.2s
[CV 1/5] END learning_rate=0.7, max_depth=5, n_estimators=50;, score=0.827 total time=   0.2s
[CV 2/5] END learning_rate=0.7, max_depth=5, n_estimators=50;, score=0.875 total time=   0.2s
[CV 3/5] END learning_rate=0.7, max_depth=5, n_estimators=50;, score=0.875 total time=   0.2s
[CV 4/5] END learning_rate=0.7, max_depth=5, n_estimators=50;, score=0.842 total time=   0.2s
[CV 5/5] END learning_rate=0.7, max_depth=5, n_estimators=50;, score=0.818 total time=   0.2s
[CV 1/5] END learning_rate=0.7, max_depth=5, n_estimators=75;, score=0.827 total time=   0.3s
[CV 2/5] END learning_rate=0.7, max_depth=5, n_estimators=75;, score=0.875 total time=   0.3s
[CV 3/5] END learning_rate=0.7, max_depth=5, n_estimators=75;, score=0.875 total time=   0.3s
[CV 4/5] END learning_rate=0.7, max_depth=5, n_estimators=75;, score=0.843 total time=   0.3s
[CV 5/5] END learning_rate=0.7, max_depth=5, n_estimators=75;, score=0.818 total time=   0.3s
[CV 1/5] END learning_rate=0.7, max_depth=5, n_estimators=100;, score=0.827 total time=   0.4s
[CV 2/5] END learning_rate=0.7, max_depth=5, n_estimators=100;, score=0.875 total time=   0.5s
[CV 3/5] END learning_rate=0.7, max_depth=5, n_estimators=100;, score=0.875 total time=   0.4s
[CV 4/5] END learning_rate=0.7, max_depth=5, n_estimators=100;, score=0.842 total time=   0.4s
[CV 5/5] END learning_rate=0.7, max_depth=5, n_estimators=100;, score=0.818 total time=   0.6s
{'learning_rate': 0.17575, 'max_depth': 3, 'n_estimators': 100}
0.896481258120897


pip install xgboost

Collecting xgboost
  Obtaining dependency information for xgboost from https://files.pythonhosted.org/packages/24/ec/ad387100fa3cc2b9b81af0829b5ecfe75ec5bb19dd7c19d4fea06fb81802/xgboost-2.0.3-py3-none-win_amd64.whl.metadata
  Downloading xgboost-2.0.3-py3-none-win_amd64.whl.metadata (2.0 kB)
Requirement already satisfied: numpy in c:\users\amansh\anaconda3\lib\site-packages (from xgboost) (1.24.3)
Requirement already satisfied: scipy in c:\users\amansh\anaconda3\lib\site-packages (from xgboost) (1.11.1)
Downloading xgboost-2.0.3-py3-none-win_amd64.whl (99.8 MB)
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Installing collected packages: xgboost
Successfully installed xgboost-2.0.3
Note: you may need to restart the kernel to use updated packages.


# prompt: write a xgb boost classifier for dataset HR_comma_sep.csv in which i have this parameters params={'n_estimators':[50,75,100],'learning_rate':np.linspace(0.001,0.7,5),'max_depth':[None,2,3,5]}

#importing the necessary libraries
import numpy as np
import pandas as pd
from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV,StratifiedKFold
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
import os
os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\human-resources-analytics')



hr = pd.read_csv('HR_comma_sep.csv')

# Create dummy variables for categorical features
dum_hr = pd.get_dummies(hr, drop_first=True)

# Separate features (x) and target variable (y)
X = dum_hr.drop('left', axis=1)
y = dum_hr['left']



gbm=XGBClassifier(random_state=23)
kfold=StratifiedKFold(n_splits=5,shuffle=True,random_state=23)

params={'n_estimators':[50,75,100],'learning_rate':np.linspace(0.001,0.7,5),'max_depth':[None,2,3,5]}
gcv=GridSearchCV(gbm,param_grid=params,cv=kfold,verbose=3,scoring='neg_log_loss')

gcv.fit(X,y)
print(gcv.best_params_)
print(gcv.best_score_)

Fitting 5 folds for each of 60 candidates, totalling 300 fits
[CV 1/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=-0.512 total time=   0.1s
[CV 2/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=-0.511 total time=   0.1s
[CV 3/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=-0.511 total time=   0.1s
[CV 4/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=-0.512 total time=   0.1s
[CV 5/5] END learning_rate=0.001, max_depth=None, n_estimators=50;, score=-0.511 total time=   0.1s
[CV 1/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=-0.494 total time=   0.1s
[CV 2/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=-0.494 total time=   0.2s
[CV 3/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=-0.493 total time=   0.1s
[CV 4/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=-0.494 total time=   0.1s
[CV 5/5] END learning_rate=0.001, max_depth=None, n_estimators=75;, score=-0.494 total time=   0.1s
[CV 1/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=-0.478 total time=   0.2s
[CV 2/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=-0.478 total time=   0.2s
[CV 3/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=-0.477 total time=   0.2s
[CV 4/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=-0.478 total time=   0.2s
[CV 5/5] END learning_rate=0.001, max_depth=None, n_estimators=100;, score=-0.477 total time=   0.2s
[CV 1/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=-0.528 total time=   0.0s
[CV 2/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=-0.528 total time=   0.0s
[CV 3/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=-0.528 total time=   0.0s
[CV 4/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=-0.528 total time=   0.0s
[CV 5/5] END learning_rate=0.001, max_depth=2, n_estimators=50;, score=-0.528 total time=   0.0s
[CV 1/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=-0.518 total time=   0.1s
[CV 2/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=-0.518 total time=   0.1s
[CV 3/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=-0.518 total time=   0.1s
[CV 4/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=-0.518 total time=   0.1s
[CV 5/5] END learning_rate=0.001, max_depth=2, n_estimators=75;, score=-0.518 total time=   0.1s
[CV 1/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=-0.509 total time=   0.1s
[CV 2/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=-0.508 total time=   0.1s
[CV 3/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=-0.509 total time=   0.1s
[CV 4/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=-0.509 total time=   0.1s
[CV 5/5] END learning_rate=0.001, max_depth=2, n_estimators=100;, score=-0.509 total time=   0.1s
[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-0.516 total time=   0.0s
[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-0.515 total time=   0.0s
[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-0.516 total time=   0.0s
[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-0.516 total time=   0.0s
[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-0.515 total time=   0.0s
[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=-0.500 total time=   0.1s
[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=-0.500 total time=   0.1s
[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=-0.500 total time=   0.1s
[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=-0.501 total time=   0.1s
[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=75;, score=-0.499 total time=   0.1s
[CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-0.486 total time=   0.1s
[CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-0.485 total time=   0.1s
[CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-0.486 total time=   0.1s
[CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-0.487 total time=   0.1s
[CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-0.485 total time=   0.1s
[CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-0.512 total time=   0.1s
[CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-0.512 total time=   0.0s
[CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-0.512 total time=   0.1s
[CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-0.512 total time=   0.1s
[CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-0.512 total time=   0.1s
[CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=-0.495 total time=   0.1s
[CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=-0.495 total time=   0.1s
[CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=-0.494 total time=   0.1s
[CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=-0.495 total time=   0.1s
[CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=75;, score=-0.495 total time=   0.1s
[CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-0.479 total time=   0.1s
[CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-0.479 total time=   0.1s
[CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-0.478 total time=   0.1s
[CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-0.480 total time=   0.1s
[CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-0.478 total time=   0.1s
[CV 1/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=-0.074 total time=   0.1s
[CV 2/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=-0.068 total time=   0.1s
[CV 3/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=-0.059 total time=   0.1s
[CV 4/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=-0.067 total time=   0.1s
[CV 5/5] END learning_rate=0.17575, max_depth=None, n_estimators=50;, score=-0.066 total time=   0.1s
[CV 1/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=-0.068 total time=   0.1s
[CV 2/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=-0.063 total time=   0.1s
[CV 3/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=-0.054 total time=   0.1s
[CV 4/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=-0.061 total time=   0.1s
[CV 5/5] END learning_rate=0.17575, max_depth=None, n_estimators=75;, score=-0.061 total time=   0.1s
[CV 1/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=-0.064 total time=   0.1s
[CV 2/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=-0.058 total time=   0.2s
[CV 3/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=-0.051 total time=   0.1s
[CV 4/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=-0.056 total time=   0.1s
[CV 5/5] END learning_rate=0.17575, max_depth=None, n_estimators=100;, score=-0.059 total time=   0.1s
[CV 1/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=-0.143 total time=   0.0s
[CV 2/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=-0.139 total time=   0.0s
[CV 3/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=-0.137 total time=   0.0s
[CV 4/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=-0.149 total time=   0.0s
[CV 5/5] END learning_rate=0.17575, max_depth=2, n_estimators=50;, score=-0.137 total time=   0.0s
[CV 1/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=-0.124 total time=   0.1s
[CV 2/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=-0.117 total time=   0.0s
[CV 3/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=-0.111 total time=   0.0s
[CV 4/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=-0.124 total time=   0.0s
[CV 5/5] END learning_rate=0.17575, max_depth=2, n_estimators=75;, score=-0.114 total time=   0.0s
[CV 1/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=-0.115 total time=   0.1s
[CV 2/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=-0.107 total time=   0.1s
[CV 3/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=-0.100 total time=   0.1s
[CV 4/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=-0.112 total time=   0.1s
[CV 5/5] END learning_rate=0.17575, max_depth=2, n_estimators=100;, score=-0.104 total time=   0.1s
[CV 1/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=-0.106 total time=   0.0s
[CV 2/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=-0.096 total time=   0.0s
[CV 3/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=-0.092 total time=   0.0s
[CV 4/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=-0.100 total time=   0.0s
[CV 5/5] END learning_rate=0.17575, max_depth=3, n_estimators=50;, score=-0.095 total time=   0.1s
[CV 1/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=-0.096 total time=   0.1s
[CV 2/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=-0.087 total time=   0.1s
[CV 3/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=-0.083 total time=   0.1s
[CV 4/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=-0.091 total time=   0.1s
[CV 5/5] END learning_rate=0.17575, max_depth=3, n_estimators=75;, score=-0.086 total time=   0.2s
[CV 1/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=-0.092 total time=   0.1s
[CV 2/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=-0.084 total time=   0.1s
[CV 3/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=-0.078 total time=   0.1s
[CV 4/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=-0.086 total time=   0.1s
[CV 5/5] END learning_rate=0.17575, max_depth=3, n_estimators=100;, score=-0.082 total time=   0.1s
[CV 1/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=-0.082 total time=   0.1s
[CV 2/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=-0.075 total time=   0.1s
[CV 3/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=-0.067 total time=   0.0s
[CV 4/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=-0.076 total time=   0.1s
[CV 5/5] END learning_rate=0.17575, max_depth=5, n_estimators=50;, score=-0.073 total time=   0.1s
[CV 1/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=-0.076 total time=   0.1s
[CV 2/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=-0.070 total time=   0.1s
[CV 3/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=-0.062 total time=   0.1s
[CV 4/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=-0.070 total time=   0.1s
[CV 5/5] END learning_rate=0.17575, max_depth=5, n_estimators=75;, score=-0.069 total time=   0.1s
[CV 1/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=-0.071 total time=   0.1s
[CV 2/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=-0.065 total time=   0.1s
[CV 3/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=-0.059 total time=   0.1s
[CV 4/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=-0.066 total time=   0.1s
[CV 5/5] END learning_rate=0.17575, max_depth=5, n_estimators=100;, score=-0.066 total time=   0.1s
[CV 1/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=-0.065 total time=   0.1s
[CV 2/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=-0.058 total time=   0.1s
[CV 3/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=-0.049 total time=   0.1s
[CV 4/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=-0.055 total time=   0.0s
[CV 5/5] END learning_rate=0.3505, max_depth=None, n_estimators=50;, score=-0.061 total time=   0.1s
[CV 1/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=-0.060 total time=   0.1s
[CV 2/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=-0.053 total time=   0.1s
[CV 3/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=-0.044 total time=   0.1s
[CV 4/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=-0.049 total time=   0.1s
[CV 5/5] END learning_rate=0.3505, max_depth=None, n_estimators=75;, score=-0.057 total time=   0.1s
[CV 1/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=-0.059 total time=   0.1s
[CV 2/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=-0.052 total time=   0.1s
[CV 3/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=-0.044 total time=   0.1s
[CV 4/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=-0.047 total time=   0.1s
[CV 5/5] END learning_rate=0.3505, max_depth=None, n_estimators=100;, score=-0.057 total time=   0.3s
[CV 1/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=-0.112 total time=   0.0s
[CV 2/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=-0.102 total time=   0.0s
[CV 3/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=-0.094 total time=   0.0s
[CV 4/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=-0.103 total time=   0.0s
[CV 5/5] END learning_rate=0.3505, max_depth=2, n_estimators=50;, score=-0.103 total time=   0.0s
[CV 1/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=-0.104 total time=   0.0s
[CV 2/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=-0.095 total time=   0.0s
[CV 3/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=-0.085 total time=   0.0s
[CV 4/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=-0.095 total time=   0.0s
[CV 5/5] END learning_rate=0.3505, max_depth=2, n_estimators=75;, score=-0.091 total time=   0.0s
[CV 1/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=-0.099 total time=   0.1s
[CV 2/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=-0.090 total time=   0.1s
[CV 3/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=-0.079 total time=   0.1s
[CV 4/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=-0.091 total time=   0.1s
[CV 5/5] END learning_rate=0.3505, max_depth=2, n_estimators=100;, score=-0.088 total time=   0.1s
[CV 1/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=-0.094 total time=   0.0s
[CV 2/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=-0.085 total time=   0.0s
[CV 3/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=-0.078 total time=   0.0s
[CV 4/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=-0.086 total time=   0.0s
[CV 5/5] END learning_rate=0.3505, max_depth=3, n_estimators=50;, score=-0.078 total time=   0.0s
[CV 1/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=-0.086 total time=   0.0s
[CV 2/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=-0.080 total time=   0.1s
[CV 3/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=-0.071 total time=   0.1s
[CV 4/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=-0.079 total time=   0.1s
[CV 5/5] END learning_rate=0.3505, max_depth=3, n_estimators=75;, score=-0.074 total time=   0.1s
[CV 1/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=-0.081 total time=   0.1s
[CV 2/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=-0.075 total time=   0.1s
[CV 3/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=-0.067 total time=   0.1s
[CV 4/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=-0.075 total time=   0.1s
[CV 5/5] END learning_rate=0.3505, max_depth=3, n_estimators=100;, score=-0.071 total time=   0.1s
[CV 1/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=-0.074 total time=   0.0s
[CV 2/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=-0.066 total time=   0.0s
[CV 3/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=-0.058 total time=   0.0s
[CV 4/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=-0.062 total time=   0.1s
[CV 5/5] END learning_rate=0.3505, max_depth=5, n_estimators=50;, score=-0.064 total time=   0.0s
[CV 1/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=-0.068 total time=   0.1s
[CV 2/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=-0.061 total time=   0.1s
[CV 3/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=-0.051 total time=   0.1s
[CV 4/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=-0.055 total time=   0.1s
[CV 5/5] END learning_rate=0.3505, max_depth=5, n_estimators=75;, score=-0.060 total time=   0.1s
[CV 1/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=-0.064 total time=   0.1s
[CV 2/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=-0.056 total time=   0.1s
[CV 3/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=-0.048 total time=   0.1s
[CV 4/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=-0.051 total time=   0.1s
[CV 5/5] END learning_rate=0.3505, max_depth=5, n_estimators=100;, score=-0.060 total time=   0.1s
[CV 1/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=-0.058 total time=   0.1s
[CV 2/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=-0.056 total time=   0.1s
[CV 3/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=-0.051 total time=   0.1s
[CV 4/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=-0.052 total time=   0.1s
[CV 5/5] END learning_rate=0.52525, max_depth=None, n_estimators=50;, score=-0.061 total time=   0.1s
[CV 1/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=-0.056 total time=   0.1s
[CV 2/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=-0.051 total time=   0.1s
[CV 3/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=-0.050 total time=   0.1s
[CV 4/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=-0.050 total time=   0.1s
[CV 5/5] END learning_rate=0.52525, max_depth=None, n_estimators=75;, score=-0.061 total time=   0.1s
[CV 1/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=-0.058 total time=   0.2s
[CV 2/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=-0.052 total time=   0.2s
[CV 3/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=-0.050 total time=   0.2s
[CV 4/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=-0.052 total time=   0.2s
[CV 5/5] END learning_rate=0.52525, max_depth=None, n_estimators=100;, score=-0.064 total time=   0.2s
[CV 1/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=-0.101 total time=   0.0s
[CV 2/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=-0.095 total time=   0.0s
[CV 3/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=-0.087 total time=   0.0s
[CV 4/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=-0.094 total time=   0.0s
[CV 5/5] END learning_rate=0.52525, max_depth=2, n_estimators=50;, score=-0.090 total time=   0.0s
[CV 1/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=-0.097 total time=   0.0s
[CV 2/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=-0.090 total time=   0.0s
[CV 3/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=-0.081 total time=   0.0s
[CV 4/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=-0.089 total time=   0.0s
[CV 5/5] END learning_rate=0.52525, max_depth=2, n_estimators=75;, score=-0.084 total time=   0.0s
[CV 1/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=-0.093 total time=   0.1s
[CV 2/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=-0.087 total time=   0.1s
[CV 3/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=-0.076 total time=   0.1s
[CV 4/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=-0.083 total time=   0.1s
[CV 5/5] END learning_rate=0.52525, max_depth=2, n_estimators=100;, score=-0.079 total time=   0.1s
[CV 1/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=-0.082 total time=   0.0s
[CV 2/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=-0.079 total time=   0.0s
[CV 3/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=-0.073 total time=   0.0s
[CV 4/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=-0.078 total time=   0.0s
[CV 5/5] END learning_rate=0.52525, max_depth=3, n_estimators=50;, score=-0.074 total time=   0.0s
[CV 1/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=-0.075 total time=   0.1s
[CV 2/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=-0.075 total time=   0.1s
[CV 3/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=-0.066 total time=   0.1s
[CV 4/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=-0.076 total time=   0.1s
[CV 5/5] END learning_rate=0.52525, max_depth=3, n_estimators=75;, score=-0.071 total time=   0.1s
[CV 1/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=-0.071 total time=   0.1s
[CV 2/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=-0.071 total time=   0.1s
[CV 3/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=-0.063 total time=   0.1s
[CV 4/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=-0.073 total time=   0.1s
[CV 5/5] END learning_rate=0.52525, max_depth=3, n_estimators=100;, score=-0.070 total time=   0.1s
[CV 1/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=-0.067 total time=   0.1s
[CV 2/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=-0.062 total time=   0.0s
[CV 3/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=-0.054 total time=   0.0s
[CV 4/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=-0.053 total time=   0.0s
[CV 5/5] END learning_rate=0.52525, max_depth=5, n_estimators=50;, score=-0.060 total time=   0.1s
[CV 1/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=-0.066 total time=   0.1s
[CV 2/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=-0.057 total time=   0.1s
[CV 3/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=-0.051 total time=   0.1s
[CV 4/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=-0.051 total time=   0.1s
[CV 5/5] END learning_rate=0.52525, max_depth=5, n_estimators=75;, score=-0.057 total time=   0.1s
[CV 1/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=-0.065 total time=   0.1s
[CV 2/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=-0.056 total time=   0.1s
[CV 3/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=-0.051 total time=   0.1s
[CV 4/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=-0.050 total time=   0.1s
[CV 5/5] END learning_rate=0.52525, max_depth=5, n_estimators=100;, score=-0.059 total time=   0.1s
[CV 1/5] END learning_rate=0.7, max_depth=None, n_estimators=50;, score=-0.062 total time=   0.1s
[CV 2/5] END learning_rate=0.7, max_depth=None, n_estimators=50;, score=-0.052 total time=   0.1s
[CV 3/5] END learning_rate=0.7, max_depth=None, n_estimators=50;, score=-0.043 total time=   0.1s
[CV 4/5] END learning_rate=0.7, max_depth=None, n_estimators=50;, score=-0.051 total time=   0.1s
[CV 5/5] END learning_rate=0.7, max_depth=None, n_estimators=50;, score=-0.065 total time=   0.1s
[CV 1/5] END learning_rate=0.7, max_depth=None, n_estimators=75;, score=-0.065 total time=   0.1s
[CV 2/5] END learning_rate=0.7, max_depth=None, n_estimators=75;, score=-0.051 total time=   0.1s
[CV 3/5] END learning_rate=0.7, max_depth=None, n_estimators=75;, score=-0.045 total time=   0.1s
[CV 4/5] END learning_rate=0.7, max_depth=None, n_estimators=75;, score=-0.054 total time=   0.2s
[CV 5/5] END learning_rate=0.7, max_depth=None, n_estimators=75;, score=-0.071 total time=   0.1s
[CV 1/5] END learning_rate=0.7, max_depth=None, n_estimators=100;, score=-0.066 total time=   0.2s
[CV 2/5] END learning_rate=0.7, max_depth=None, n_estimators=100;, score=-0.053 total time=   0.1s
[CV 3/5] END learning_rate=0.7, max_depth=None, n_estimators=100;, score=-0.046 total time=   0.1s
[CV 4/5] END learning_rate=0.7, max_depth=None, n_estimators=100;, score=-0.056 total time=   0.2s
[CV 5/5] END learning_rate=0.7, max_depth=None, n_estimators=100;, score=-0.073 total time=   0.2s
[CV 1/5] END learning_rate=0.7, max_depth=2, n_estimators=50;, score=-0.100 total time=   0.0s
[CV 2/5] END learning_rate=0.7, max_depth=2, n_estimators=50;, score=-0.091 total time=   0.0s
[CV 3/5] END learning_rate=0.7, max_depth=2, n_estimators=50;, score=-0.078 total time=   0.0s
[CV 4/5] END learning_rate=0.7, max_depth=2, n_estimators=50;, score=-0.094 total time=   0.0s
[CV 5/5] END learning_rate=0.7, max_depth=2, n_estimators=50;, score=-0.084 total time=   0.0s
[CV 1/5] END learning_rate=0.7, max_depth=2, n_estimators=75;, score=-0.095 total time=   0.0s
[CV 2/5] END learning_rate=0.7, max_depth=2, n_estimators=75;, score=-0.086 total time=   0.0s
[CV 3/5] END learning_rate=0.7, max_depth=2, n_estimators=75;, score=-0.075 total time=   0.0s
[CV 4/5] END learning_rate=0.7, max_depth=2, n_estimators=75;, score=-0.088 total time=   0.0s
[CV 5/5] END learning_rate=0.7, max_depth=2, n_estimators=75;, score=-0.078 total time=   0.0s
[CV 1/5] END learning_rate=0.7, max_depth=2, n_estimators=100;, score=-0.092 total time=   0.1s
[CV 2/5] END learning_rate=0.7, max_depth=2, n_estimators=100;, score=-0.085 total time=   0.1s
[CV 3/5] END learning_rate=0.7, max_depth=2, n_estimators=100;, score=-0.071 total time=   0.1s
[CV 4/5] END learning_rate=0.7, max_depth=2, n_estimators=100;, score=-0.084 total time=   0.1s
[CV 5/5] END learning_rate=0.7, max_depth=2, n_estimators=100;, score=-0.078 total time=   0.1s
[CV 1/5] END learning_rate=0.7, max_depth=3, n_estimators=50;, score=-0.078 total time=   0.0s
[CV 2/5] END learning_rate=0.7, max_depth=3, n_estimators=50;, score=-0.075 total time=   0.1s
[CV 3/5] END learning_rate=0.7, max_depth=3, n_estimators=50;, score=-0.069 total time=   0.0s
[CV 4/5] END learning_rate=0.7, max_depth=3, n_estimators=50;, score=-0.073 total time=   0.0s
[CV 5/5] END learning_rate=0.7, max_depth=3, n_estimators=50;, score=-0.070 total time=   0.0s
[CV 1/5] END learning_rate=0.7, max_depth=3, n_estimators=75;, score=-0.072 total time=   0.1s
[CV 2/5] END learning_rate=0.7, max_depth=3, n_estimators=75;, score=-0.071 total time=   0.1s
[CV 3/5] END learning_rate=0.7, max_depth=3, n_estimators=75;, score=-0.066 total time=   0.1s
[CV 4/5] END learning_rate=0.7, max_depth=3, n_estimators=75;, score=-0.069 total time=   0.1s
[CV 5/5] END learning_rate=0.7, max_depth=3, n_estimators=75;, score=-0.069 total time=   0.1s
[CV 1/5] END learning_rate=0.7, max_depth=3, n_estimators=100;, score=-0.070 total time=   0.1s
[CV 2/5] END learning_rate=0.7, max_depth=3, n_estimators=100;, score=-0.068 total time=   0.1s
[CV 3/5] END learning_rate=0.7, max_depth=3, n_estimators=100;, score=-0.064 total time=   0.1s
[CV 4/5] END learning_rate=0.7, max_depth=3, n_estimators=100;, score=-0.063 total time=   0.1s
[CV 5/5] END learning_rate=0.7, max_depth=3, n_estimators=100;, score=-0.068 total time=   0.1s
[CV 1/5] END learning_rate=0.7, max_depth=5, n_estimators=50;, score=-0.068 total time=   0.1s
[CV 2/5] END learning_rate=0.7, max_depth=5, n_estimators=50;, score=-0.056 total time=   0.1s
[CV 3/5] END learning_rate=0.7, max_depth=5, n_estimators=50;, score=-0.055 total time=   0.1s
[CV 4/5] END learning_rate=0.7, max_depth=5, n_estimators=50;, score=-0.056 total time=   0.1s
[CV 5/5] END learning_rate=0.7, max_depth=5, n_estimators=50;, score=-0.061 total time=   0.1s
[CV 1/5] END learning_rate=0.7, max_depth=5, n_estimators=75;, score=-0.065 total time=   0.1s
[CV 2/5] END learning_rate=0.7, max_depth=5, n_estimators=75;, score=-0.053 total time=   0.1s
[CV 3/5] END learning_rate=0.7, max_depth=5, n_estimators=75;, score=-0.054 total time=   0.1s
[CV 4/5] END learning_rate=0.7, max_depth=5, n_estimators=75;, score=-0.054 total time=   0.1s
[CV 5/5] END learning_rate=0.7, max_depth=5, n_estimators=75;, score=-0.063 total time=   0.1s
[CV 1/5] END learning_rate=0.7, max_depth=5, n_estimators=100;, score=-0.065 total time=   0.1s
[CV 2/5] END learning_rate=0.7, max_depth=5, n_estimators=100;, score=-0.053 total time=   0.1s
[CV 3/5] END learning_rate=0.7, max_depth=5, n_estimators=100;, score=-0.054 total time=   0.1s
[CV 4/5] END learning_rate=0.7, max_depth=5, n_estimators=100;, score=-0.057 total time=   0.1s
[CV 5/5] END learning_rate=0.7, max_depth=5, n_estimators=100;, score=-0.066 total time=   0.1s
{'learning_rate': 0.3505, 'max_depth': None, 'n_estimators': 100}
-0.05169510616191343


# Set the working directory
os.chdir("C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Satellite Imaging")

# Load the dataset
df = pd.read_csv('Satellite.csv',sep=';')

# Separate features (X) and target variable (y)
x = df.drop('classes', axis=1)
y = df['classes']

x


import pandas as pd 
import matplotlib.pyplot as plt 
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np 
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import StackingClassifier 
from sklearn.svm import SVC 
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as lda
import os
os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Bankruptcy')

hr = pd.read_csv("Bankruptcy.csv",index_col=0)
X = hr.drop('D', axis=1)
y = hr['D']

lr = LogisticRegression()
lda= LinearDiscriminantAnalysis()
dtc = DecisionTreeClassifier(random_state=23)
rf = RandomForestClassifier(random_state=23,
                            n_estimators=25)

stack = StackingClassifier(estimators=[('LR', lr),
                                       ('SVM', svm),
                                       ('TREE', dtc)],
                           final_estimator=rf)

print(stack.get_params())
params={'LR_C':[0.1,0.5,1,2],'TREE_max_depth':[None,3,5],'final_estimator_max_fetaures':[2,5,10],'passthrough':[True,False]}
kfold=StratifiedKFold(n_splits=5,shuffle=True,random_state=23)
gcv=GridSearchCV(stack,param_grid=params,cv-kfold,scoring='neg_log_loss')

gcv.fit(X,y)
print(gcv.best_params_)
print(gcv.best_score_)

  Cell In[6], line 35
    gcv=GridSearchCV(stack,param_grid=params,cv-kfold,scoring='neg_log_loss')
                                                                            ^
SyntaxError: positional argument follows keyword argument


import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import StackingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

import os

os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Bankruptcy')

# Assuming 'Bankruptcy.csv' file contains your data
hr = pd.read_csv("Bankruptcy.csv", index_col=0)

X = hr.drop('D', axis=1)
y = hr['D']


# Define classifiers
lr = LogisticRegression()
lda = LDA()
dtc = DecisionTreeClassifier(random_state=23)
rf = RandomForestClassifier(random_state=23, n_estimators=25)

# Stacking Classifier
stack = StackingClassifier(estimators=[('LR', lr), ('SVM', SVC()), ('TREE', dtc)], final_estimator=rf,'passthrough':[True,False])

# Fit the stack classifier on the training data
stack.fit(X_train, y_train)

# Make predictions on the test data
y_pred = stack.predict(X_test)

# Evaluate accuracy
print("Accuracy:", accuracy_score(y_test, y_pred))

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Accuracy: 0.7407407407407407


import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis  # Fix the import
import os

os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Bankruptcy')

hr = pd.read_csv("Bankruptcy.csv", index_col=0)
X = hr.drop('D', axis=1)
y = hr['D']

lr = LogisticRegression()
lda = LinearDiscriminantAnalysis()  # Fix the variable name
dtc = DecisionTreeClassifier(random_state=23)
rf = RandomForestClassifier(random_state=23, n_estimators=25)

stack = StackingClassifier(estimators=[('LR', lr),
                                       ('SVM', SVC()),
                                       ('TREE', dtc)],
                           final_estimator=rf)

print(stack.get_params())
params = {'LR__C': [0.1, 0.5, 1, 2], 'TREE__max_depth': [None, 3, 5],
          'final_estimator__max_features': [2, 5, 10], 'passthrough': [True, False]}
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=23)
gcv = GridSearchCV(stack, param_grid=params, cv=kfold, scoring='neg_log_loss')

gcv.fit(X, y)
print(gcv.best_params_)
print(gcv.best_score_)

{'cv': None, 'estimators': [('LR', LogisticRegression()), ('SVM', SVC()), ('TREE', DecisionTreeClassifier(random_state=23))], 'final_estimator__bootstrap': True, 'final_estimator__ccp_alpha': 0.0, 'final_estimator__class_weight': None, 'final_estimator__criterion': 'gini', 'final_estimator__max_depth': None, 'final_estimator__max_features': 'sqrt', 'final_estimator__max_leaf_nodes': None, 'final_estimator__max_samples': None, 'final_estimator__min_impurity_decrease': 0.0, 'final_estimator__min_samples_leaf': 1, 'final_estimator__min_samples_split': 2, 'final_estimator__min_weight_fraction_leaf': 0.0, 'final_estimator__n_estimators': 25, 'final_estimator__n_jobs': None, 'final_estimator__oob_score': False, 'final_estimator__random_state': 23, 'final_estimator__verbose': 0, 'final_estimator__warm_start': False, 'final_estimator': RandomForestClassifier(n_estimators=25, random_state=23), 'n_jobs': None, 'passthrough': False, 'stack_method': 'auto', 'verbose': 0, 'LR': LogisticRegression(), 'SVM': SVC(), 'TREE': DecisionTreeClassifier(random_state=23), 'LR__C': 1.0, 'LR__class_weight': None, 'LR__dual': False, 'LR__fit_intercept': True, 'LR__intercept_scaling': 1, 'LR__l1_ratio': None, 'LR__max_iter': 100, 'LR__multi_class': 'auto', 'LR__n_jobs': None, 'LR__penalty': 'l2', 'LR__random_state': None, 'LR__solver': 'lbfgs', 'LR__tol': 0.0001, 'LR__verbose': 0, 'LR__warm_start': False, 'SVM__C': 1.0, 'SVM__break_ties': False, 'SVM__cache_size': 200, 'SVM__class_weight': None, 'SVM__coef0': 0.0, 'SVM__decision_function_shape': 'ovr', 'SVM__degree': 3, 'SVM__gamma': 'scale', 'SVM__kernel': 'rbf', 'SVM__max_iter': -1, 'SVM__probability': False, 'SVM__random_state': None, 'SVM__shrinking': True, 'SVM__tol': 0.001, 'SVM__verbose': False, 'TREE__ccp_alpha': 0.0, 'TREE__class_weight': None, 'TREE__criterion': 'gini', 'TREE__max_depth': None, 'TREE__max_features': None, 'TREE__max_leaf_nodes': None, 'TREE__min_impurity_decrease': 0.0, 'TREE__min_samples_leaf': 1, 'TREE__min_samples_split': 2, 'TREE__min_weight_fraction_leaf': 0.0, 'TREE__random_state': 23, 'TREE__splitter': 'best'}

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

{'LR__C': 2, 'TREE__max_depth': 5, 'final_estimator__max_features': 2, 'passthrough': True}
-0.38356607654373853

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import os

os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Datasets')

milk=pd.read_csv('milk.csv',index_col=0)
scaler=StandardScaler()
m_scaled=scaler.fit_transform(milk)

prcomp=PCA()
comps=prcomp.fit_transform(m_scaled)
print(milk.shape)
print(comps.shape)

df_comps=pd.DataFrame(comps,columns=['PC1','PC2','PC3','PC4','PC5'],index=milk.index)


print(df_comps.var())

print(prcomp.explained_variance_)
tot_var=np.sum(prcomp.explained_variance_)
print(tot_var)

prop_var=np.array(prcomp.explained_variance_/tot_var)
print(prop_var)
per_var=prop_var*100
print(per_var)

print('per_vr_Explained by 1st two pcs:')

print(prcomp.explained_variance_ratio_)

(25, 5)
(25, 5)
PC1    4.040412
PC2    0.924372
PC3    0.134291
PC4    0.102689
PC5    0.006570
dtype: float64
[4.0404117  0.92437238 0.13429088 0.1026886  0.00656977]
5.208333333333335
[0.77575905 0.1774795  0.02578385 0.01971621 0.0012614 ]
[77.57590469 17.74794969  2.57838496  1.97162103  0.12613963]
per_vr_Explained by 1st two pcs:
[0.77575905 0.1774795  0.02578385 0.01971621 0.0012614 ]


from sklearn.pipeline import Pipeline

scaler=StandardScaler()
prcomp=PCA()
pipe=Pipeline()

(25, 5)
(25, 5)
PC1    4.040412
PC2    0.924372
PC3    0.134291
PC4    0.102689
PC5    0.006570
dtype: float64
[4.0404117  0.92437238 0.13429088 0.1026886  0.00656977]
5.208333333333335
[0.77575905 0.1774795  0.02578385 0.01971621 0.0012614 ]
[77.57590469 17.74794969  2.57838496  1.97162103  0.12613963]
Percentage of Variance Explained by 1st two PCs: 95.32385437937002


from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import os

os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Datasets')

br=pd.read_csv('Bankruptcy.csv',index_col=0)
scaler=StandardScaler()
m_scaled=scaler.fit_transform(milk)

prcomp=PCA()
comps=prcomp.fit_transform(m_scaled)
print(milk.shape)
print(comps.shape)

df_comps=pd.DataFrame(comps,columns=['PC1','PC2','PC3','PC4','PC5'],index=milk.index)


print(df_comps.var())

print(prcomp.explained_variance_)
tot_var=np.sum(prcomp.explained_variance_)
print(tot_var)

prop_var=np.array(prcomp.explained_variance_/tot_var)
print(prop_var)
per_var=prop_var*100
print(per_var)

print('per_vr_Explained by 1st two pcs:')

print(prcomp.explained_variance_ratio_)


os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\Bankruptcy')
df=pd.read_csv('Bankruptcy.csv',index_col=0)
df.columns

Index(['D', 'YR', 'R1', 'R2', 'R3', 'R4', 'R5', 'R6', 'R7', 'R8', 'R9', 'R10',
       'R11', 'R12', 'R13', 'R14', 'R15', 'R16', 'R17', 'R18', 'R19', 'R20',
       'R21', 'R22', 'R23', 'R24'],
      dtype='object')


from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
import pandas as pd
import os

os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Bankruptcy')

# Load the dataset
bankruptcy = pd.read_csv('Bankruptcy.csv', index_col=0)

# Separate features (X) and target variable (y)
X = bankruptcy.drop('D', axis=1)
y = bankruptcy['D']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(comps, y, test_size=0.3, random_state=23)


# Create a pipeline with StandardScaler and PCA
pipe = Pipeline([
    ('SCL', StandardScaler()),
    ('PCA', PCA(n_components=2))  # Set the number of components to 2
])

# Fit and transform the data using the pipeline
comps = pipe.fit_transform(X_train)

cum_sum=np.cumsum(prcomp.explained_variance_ratio_*100)


X_trn_pc=comps[:,:10]
X_tst_pc=pipe.transform(X_test)[:,:10]

lr=LogisticRegression()
lr.fit(X_trn_pc,y_train)
y_pred=lr.predict(X_tst_pc)
y_pred_prob=lr.predict_log_proba(X_tst_pc)

print(log_loss(y_test,y_pred_prob))

#prcomp=PCA(n_components=10)
#pipe=Pipeline[]

#print(f'Log Loss for classes 8, 9, and 11: {loss}')

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[35], line 20
     17 y = bankruptcy['D']
     19 # Split the data into training and testing sets
---> 20 X_train, X_test, y_train, y_test = train_test_split(comps, y, test_size=0.3, random_state=23)
     23 # Create a pipeline with StandardScaler and PCA
     24 pipe = Pipeline([
     25     ('SCL', StandardScaler()),
     26     ('PCA', PCA(n_components=2))  # Set the number of components to 2
     27 ])

File ~\anaconda3\Lib\site-packages\sklearn\utils\_param_validation.py:214, in validate_params.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    208 try:
    209     with config_context(
    210         skip_parameter_validation=(
    211             prefer_skip_nested_validation or global_skip_validation
    212         )
    213     ):
--> 214         return func(*args, **kwargs)
    215 except InvalidParameterError as e:
    216     # When the function is just a wrapper around an estimator, we allow
    217     # the function to delegate validation to the estimator, but we replace
    218     # the name of the estimator by the name of the function in the error
    219     # message to avoid confusion.
    220     msg = re.sub(
    221         r"parameter of \w+ must be",
    222         f"parameter of {func.__qualname__} must be",
    223         str(e),
    224     )

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_split.py:2646, in train_test_split(test_size, train_size, random_state, shuffle, stratify, *arrays)
   2643 if n_arrays == 0:
   2644     raise ValueError("At least one array required as input")
-> 2646 arrays = indexable(*arrays)
   2648 n_samples = _num_samples(arrays[0])
   2649 n_train, n_test = _validate_shuffle_split(
   2650     n_samples, test_size, train_size, default_test_size=0.25
   2651 )

File ~\anaconda3\Lib\site-packages\sklearn\utils\validation.py:453, in indexable(*iterables)
    434 """Make arrays indexable for cross-validation.
    435 
    436 Checks consistent length, passes through None, and ensures that everything
   (...)
    449     sparse matrix, or dataframe) or `None`.
    450 """
    452 result = [_make_indexable(X) for X in iterables]
--> 453 check_consistent_length(*result)
    454 return result

File ~\anaconda3\Lib\site-packages\sklearn\utils\validation.py:407, in check_consistent_length(*arrays)
    405 uniques = np.unique(lengths)
    406 if len(uniques) > 1:
--> 407     raise ValueError(
    408         "Found input variables with inconsistent numbers of samples: %r"
    409         % [int(l) for l in lengths]
    410     )

ValueError: Found input variables with inconsistent numbers of samples: [92, 132]


from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
import pandas as pd
import os
import numpy as np

os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Cases\\Bankruptcy')

# Load the dataset
bankruptcy = pd.read_csv('Bankruptcy.csv', index_col=0)

# Separate features (X) and target variable (y)
X = bankruptcy.drop('D', axis=1)
y = bankruptcy['D']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=23)

# Create a pipeline with StandardScaler and PCA
pipe = Pipeline([
    ('SCL', StandardScaler()),
    ('PCA', PCA(n_components=11))  # Set the number of components to 10
])

# Fit and transform the data using the pipeline
comps = pipe.fit_transform(X_train)

cum_sum = np.cumsum(pipe.named_steps['PCA'].explained_variance_ratio_ * 100)

X_trn_pc = comps[:, :10]
X_tst_pc = pipe.transform(X_test)[:, :10]

lr = LogisticRegression()
lr.fit(X_trn_pc, y_train)
y_pred = lr.predict(X_tst_pc)
y_pred_prob = lr.predict_proba(X_tst_pc)

print(log_loss(y_test, y_pred_prob))

# Uncomment and correct if needed
# prcomp = PCA(n_components=10)
# pipe = Pipeline([])

# print(f'Log Loss for classes 8, 9, and 11: {loss}')

0.5984598808150142


#Entitr pipeline
prcomp = PCA(n_components=10)
pipe = Pipeline([('SCL',StandardScaler()),('PCA',prcomp),('LR',lr)])

pipe.fit(X_train,y_train)
y_pred_prob=pipe.predict_proba(X_test)

print(log_loss(y_test,y_pred_prob))

0.5984598808150142


kfold=StratifiedKFold(n_splits=5,shuffle=True,random_state=23)
print(pipe.get_params())
params={'PCA__n_componenets':[5,6,7,8,9,10]}
gcv=GridSearchCV(pipe,param_grid=params,cv=kfold,scoring='neg_log_loss')

gcv.fit(X,y)
print(gcv.best_params_)
print(gcv.best_Score_)

{'memory': None, 'steps': [('SCL', StandardScaler()), ('PCA', PCA(n_components=10)), ('LR', LogisticRegression())], 'verbose': False, 'SCL': StandardScaler(), 'PCA': PCA(n_components=10), 'LR': LogisticRegression(), 'SCL__copy': True, 'SCL__with_mean': True, 'SCL__with_std': True, 'PCA__copy': True, 'PCA__iterated_power': 'auto', 'PCA__n_components': 10, 'PCA__n_oversamples': 10, 'PCA__power_iteration_normalizer': 'auto', 'PCA__random_state': None, 'PCA__svd_solver': 'auto', 'PCA__tol': 0.0, 'PCA__whiten': False, 'LR__C': 1.0, 'LR__class_weight': None, 'LR__dual': False, 'LR__fit_intercept': True, 'LR__intercept_scaling': 1, 'LR__l1_ratio': None, 'LR__max_iter': 100, 'LR__multi_class': 'auto', 'LR__n_jobs': None, 'LR__penalty': 'l2', 'LR__random_state': None, 'LR__solver': 'lbfgs', 'LR__tol': 0.0001, 'LR__verbose': 0, 'LR__warm_start': False}

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[46], line 6
      3 params={'PCA__n_componenets':[5,6,7,8,9,10]}
      4 gcv=GridSearchCV(pipe,param_grid=params,cv=kfold,scoring='neg_log_loss')
----> 6 gcv.fit(X,y)
      7 print(gcv.best_params_)
      8 print(gcv.best_Score_)

File ~\anaconda3\Lib\site-packages\sklearn\base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
-> 1152     return fit_method(estimator, *args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:898, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    892     results = self._format_results(
    893         all_candidate_params, n_splits, all_out, all_more_results
    894     )
    896     return results
--> 898 self._run_search(evaluate_candidates)
    900 # multimetric is determined here because in the case of a callable
    901 # self.scoring the return type is only known after calling
    902 first_test_score = all_out[0]["test_scores"]

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:1422, in GridSearchCV._run_search(self, evaluate_candidates)
   1420 def _run_search(self, evaluate_candidates):
   1421     """Search all candidates in param_grid"""
-> 1422     evaluate_candidates(ParameterGrid(self.param_grid))

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:845, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    837 if self.verbose > 0:
    838     print(
    839         "Fitting {0} folds for each of {1} candidates,"
    840         " totalling {2} fits".format(
    841             n_splits, n_candidates, n_candidates * n_splits
    842         )
    843     )
--> 845 out = parallel(
    846     delayed(_fit_and_score)(
    847         clone(base_estimator),
    848         X,
    849         y,
    850         train=train,
    851         test=test,
    852         parameters=parameters,
    853         split_progress=(split_idx, n_splits),
    854         candidate_progress=(cand_idx, n_candidates),
    855         **fit_and_score_kwargs,
    856     )
    857     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    858         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    859     )
    860 )
    862 if len(out) < 1:
    863     raise ValueError(
    864         "No fits were performed. "
    865         "Was the CV iterator empty? "
    866         "Were there no candidates?"
    867     )

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:65, in Parallel.__call__(self, iterable)
     60 config = get_config()
     61 iterable_with_config = (
     62     (_with_config(delayed_func, config), args, kwargs)
     63     for delayed_func, args, kwargs in iterable
     64 )
---> 65 return super().__call__(iterable_with_config)

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:1085, in Parallel.__call__(self, iterable)
   1076 try:
   1077     # Only set self._iterating to True if at least a batch
   1078     # was dispatched. In particular this covers the edge
   (...)
   1082     # was very quick and its callback already dispatched all the
   1083     # remaining jobs.
   1084     self._iterating = False
-> 1085     if self.dispatch_one_batch(iterator):
   1086         self._iterating = self._original_iterator is not None
   1088     while self.dispatch_one_batch(iterator):

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
    899     return False
    900 else:
--> 901     self._dispatch(tasks)
    902     return True

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:819, in Parallel._dispatch(self, batch)
    817 with self._lock:
    818     job_idx = len(self._jobs)
--> 819     job = self._backend.apply_async(batch, callback=cb)
    820     # A job can complete so quickly than its callback is
    821     # called before we get here, causing self._jobs to
    822     # grow. To ensure correct results ordering, .insert is
    823     # used (rather than .append) in the following line
    824     self._jobs.insert(job_idx, job)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
    206 def apply_async(self, func, callback=None):
    207     """Schedule a func to be run"""
--> 208     result = ImmediateResult(func)
    209     if callback:
    210         callback(result)

File ~\anaconda3\Lib\site-packages\joblib\_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
    594 def __init__(self, batch):
    595     # Don't delay the application, to avoid keeping the input
    596     # arguments in memory
--> 597     self.results = batch()

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in BatchedCalls.__call__(self)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\joblib\parallel.py:288, in <listcomp>(.0)
    284 def __call__(self):
    285     # Set the default nested backend to self._backend but do not set the
    286     # change the default number of processes to -1
    287     with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288         return [func(*args, **kwargs)
    289                 for func, args, kwargs in self.items]

File ~\anaconda3\Lib\site-packages\sklearn\utils\parallel.py:127, in _FuncWrapper.__call__(self, *args, **kwargs)
    125     config = {}
    126 with config_context(**config):
--> 127     return self.function(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:717, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    710 fit_params = _check_fit_params(X, fit_params, train)
    712 if parameters is not None:
    713     # here we clone the parameters, since sometimes the parameters
    714     # themselves might be estimators, e.g. when we search over different
    715     # estimators in a pipeline.
    716     # ref: https://github.com/scikit-learn/scikit-learn/pull/26786
--> 717     estimator = estimator.set_params(**clone(parameters, safe=False))
    719 start_time = time.time()
    721 X_train, y_train = _safe_split(estimator, X, y, train)

File ~\anaconda3\Lib\site-packages\sklearn\pipeline.py:222, in Pipeline.set_params(self, **kwargs)
    203 def set_params(self, **kwargs):
    204     """Set the parameters of this estimator.
    205 
    206     Valid parameter keys can be listed with ``get_params()``. Note that
   (...)
    220         Pipeline class instance.
    221     """
--> 222     self._set_params("steps", **kwargs)
    223     return self

File ~\anaconda3\Lib\site-packages\sklearn\utils\metaestimators.py:68, in _BaseComposition._set_params(self, attr, **params)
     65                 self._replace_estimator(attr, name, params.pop(name))
     67 # 3. Step parameters and other initialisation arguments
---> 68 super().set_params(**params)
     69 return self

File ~\anaconda3\Lib\site-packages\sklearn\base.py:263, in BaseEstimator.set_params(self, **params)
    253         warnings.warn(
    254             (
    255                 f"Parameter 'base_estimator' of {self.__class__.__name__} is"
   (...)
    260             stacklevel=2,
    261         )
    262         key = "estimator"
--> 263     valid_params[key].set_params(**sub_params)
    265 return self

File ~\anaconda3\Lib\site-packages\sklearn\base.py:230, in BaseEstimator.set_params(self, **params)
    228 if key not in valid_params:
    229     local_valid_params = self._get_param_names()
--> 230     raise ValueError(
    231         f"Invalid parameter {key!r} for estimator {self}. "
    232         f"Valid parameters are: {local_valid_params!r}."
    233     )
    235 if delim:
    236     nested_params[key][sub_key] = value

ValueError: Invalid parameter 'n_componenets' for estimator PCA(n_components=10). Valid parameters are: ['copy', 'iterated_power', 'n_components', 'n_oversamples', 'power_iteration_normalizer', 'random_state', 'svd_solver', 'tol', 'whiten'].


from sklearn.model_selection import StratifiedKFold, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
import pandas as pd

# Assuming you have loaded your data (X, y) here

# Create a pipeline with StandardScaler and PCA
pipe = Pipeline([
    ('SCL', StandardScaler()),
    ('PCA', PCA())  # No need to set n_components here
])

# Define the parameter grid
params = {'PCA__n_components': [5, 6, 7, 8, 9, 10]}  # Use 'PCA__n_components' in the parameter grid

# Define the StratifiedKFold
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=23)

# Create GridSearchCV object
gcv = GridSearchCV(pipe, param_grid=params, cv=kfold, scoring='neg_log_loss')

# Fit the model
gcv.fit(X, y)

# Print the best parameters and score
print(gcv.best_params_)
print(gcv.best_score_)

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(

{'PCA__n_components': 5}
nan

C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:821: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py", line 810, in _score
    scores = scorer(estimator, X_test, y_test)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 266, in __call__
    return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 399, in _score
    y_pred = method_caller(clf, "predict_proba", X, pos_label=self._get_pos_label())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 86, in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 212, in _get_response_values
    raise ValueError(
ValueError: Pipeline should either be a classifier to be used with response_method=predict_proba or the response_method should be 'predict'. Got a regressor with response_method=predict_proba instead.

  warnings.warn(
C:\Users\Amansh\anaconda3\Lib\site-packages\sklearn\model_selection\_search.py:979: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan nan nan]
  warnings.warn(


from pca import pca

os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Datasets')

model=pca()

milk=pd.read_csv('milk.csv',index_col=0)
scaler=StandardScaler()
m_scaled=scaler.fit_transform(milk)


results=model.fit_transform(milk)
results
comps=prcomp.fit_transform(m_scaled)
print(milk.shape)
print(comps.shape)

nan


from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import os

os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Datasets')

milk=pd.read_csv('milk.csv',index_col=0)
scaler=StandardScaler()
m_scaled=scaler.fit_transform(milk)

prcomp=PCA()
comps=prcomp.fit_transform(m_scaled)
print(milk.shape)
print(comps.shape)

df_comps=pd.DataFrame(comps,columns=['PC1','PC2','PC3','PC4','PC5'],index=milk.index)


print(df_comps.var())

print(prcomp.explained_variance_)
tot_var=np.sum(prcomp.explained_variance_)
print(tot_var)

prop_var=np.array(prcomp.explained_variance_/tot_var)
print(prop_var)
per_var=prop_var*100
print(per_var)

print('per_vr_Explained by 1st two pcs:')

print(prcomp.explained_variance_ratio_)


pip install pca

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Installing collected packages: datazets, colourmap, adjusttext, scatterd, pca
Successfully installed adjusttext-0.8 colourmap-1.1.16 datazets-0.1.9 pca-2.0.5 scatterd-1.3.7
Note: you may need to restart the kernel to use updated packages.


from scipy.cluster.hierarchy import linkage
import matplotlib.pyplot as plt


milk=pd.read_csv('milk.csv',index_col=0)
scaler=StandardScaler()
milkscaled-scaler.fit_transform(milk)

linkage_method='average'

merging=linkage(milkscaled,method='average')

dendrogram(mergings,labels=np.array(milk,index),leaf_rotation=90,leaf_font_size=10)

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
Cell In[53], line 5
      1 from scipy.cluster.hierarchy import linkage
      2 import matplotlib.pyplot as plt
----> 5 milk=pd.read_csv('milk.csv',index_col=0)
      6 scaler=StandardScaler()
      7 milkscaled-scaler.fit_transform(milk)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:912, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
    899 kwds_defaults = _refine_defaults_read(
    900     dialect,
    901     delimiter,
   (...)
    908     dtype_backend=dtype_backend,
    909 )
    910 kwds.update(kwds_defaults)
--> 912 return _read(filepath_or_buffer, kwds)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:577, in _read(filepath_or_buffer, kwds)
    574 _validate_names(kwds.get("names", None))
    576 # Create the parser.
--> 577 parser = TextFileReader(filepath_or_buffer, **kwds)
    579 if chunksize or iterator:
    580     return parser

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1407, in TextFileReader.__init__(self, f, engine, **kwds)
   1404     self.options["has_index_names"] = kwds["has_index_names"]
   1406 self.handles: IOHandles | None = None
-> 1407 self._engine = self._make_engine(f, self.engine)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1661, in TextFileReader._make_engine(self, f, engine)
   1659     if "b" not in mode:
   1660         mode += "b"
-> 1661 self.handles = get_handle(
   1662     f,
   1663     mode,
   1664     encoding=self.options.get("encoding", None),
   1665     compression=self.options.get("compression", None),
   1666     memory_map=self.options.get("memory_map", False),
   1667     is_text=is_text,
   1668     errors=self.options.get("encoding_errors", "strict"),
   1669     storage_options=self.options.get("storage_options", None),
   1670 )
   1671 assert self.handles is not None
   1672 f = self.handles.handle

File ~\anaconda3\Lib\site-packages\pandas\io\common.py:859, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    854 elif isinstance(handle, str):
    855     # Check whether the filename is to be opened in binary mode.
    856     # Binary mode does not support 'encoding' and 'newline'.
    857     if ioargs.encoding and "b" not in ioargs.mode:
    858         # Encoding
--> 859         handle = open(
    860             handle,
    861             ioargs.mode,
    862             encoding=ioargs.encoding,
    863             errors=errors,
    864             newline="",
    865         )
    866     else:
    867         # Binary mode
    868         handle = open(handle, ioargs.mode)

FileNotFoundError: [Errno 2] No such file or directory: 'milk.csv'


from scipy.cluster.hierarchy import linkage, dendrogram
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os

os.chdir('C:\\Users\\Amansh\\Downloads\\PML\\Datasets')


# Load the dataset
milk = pd.read_csv('milk.csv', index_col=0)

# Scale the data
scaler = StandardScaler()
milkscaled = scaler.fit_transform(milk)

# Choose linkage method
linkage_method = 'average'

# Perform hierarchical clustering
merging = linkage(milkscaled, method=linkage_method)

# Plot dendrogram
plt.figure(figsize=(15, 8))
dendrogram(merging, labels=np.array(milk.index), leaf_rotation=90, leaf_font_size=10)
plt.title(f'Dendrogram using {linkage_method} linkage')
plt.xlabel('Samples')
plt.ylabel('Distance')
plt.show()

	DateTime	Consumption	Production	Nuclear	Wind	Hydroelectric	Oil and Gas	Coal	Solar	Biomass
0	2019-01-01 00:00:00	6352	6527	1395	79	1383	1896	1744	0	30
1	2019-01-01 01:00:00	6116	5701	1393	96	1112	1429	1641	0	30
2	2019-01-01 02:00:00	5873	5676	1393	142	1030	1465	1616	0	30
3	2019-01-01 03:00:00	5682	5603	1397	191	972	1455	1558	0	30
4	2019-01-01 04:00:00	5557	5454	1393	159	960	1454	1458	0	30
...	...	...	...	...	...	...	...	...	...	...
36767	2023-03-12 19:00:00	6500	6643	1407	111	3084	933	1047	0	60
36768	2023-03-12 20:00:00	6471	6484	1408	58	2983	933	1042	0	60
36769	2023-03-12 21:00:00	6194	6413	1406	39	2938	935	1031	0	62
36770	2023-03-12 22:00:00	5708	6217	1406	8	2823	917	1001	0	61
36771	2023-03-12 23:00:00	5409	5900	1403	0	2492	914	1030	0	60

	DateTime	Consumption	Production	Nuclear	Wind	Hydroelectric	Oil and Gas	Coal	Solar	Biomass
0	2019-01-01 00:00:00	6352	6527	1395	79	1383	1896	1744	0	30
1	2019-01-01 01:00:00	6116	5701	1393	96	1112	1429	1641	0	30
2	2019-01-01 02:00:00	5873	5676	1393	142	1030	1465	1616	0	30
3	2019-01-01 03:00:00	5682	5603	1397	191	972	1455	1558	0	30
4	2019-01-01 04:00:00	5557	5454	1393	159	960	1454	1458	0	30
...	...	...	...	...	...	...	...	...	...	...
36767	2023-03-12 19:00:00	6500	6643	1407	111	3084	933	1047	0	60
36768	2023-03-12 20:00:00	6471	6484	1408	58	2983	933	1042	0	60
36769	2023-03-12 21:00:00	6194	6413	1406	39	2938	935	1031	0	62
36770	2023-03-12 22:00:00	5708	6217	1406	8	2823	917	1001	0	61
36771	2023-03-12 23:00:00	5409	5900	1403	0	2492	914	1030	0	60

	DateTime	Consumption	Production	Nuclear	Wind	Hydroelectric	Oil and Gas	Coal	Solar	Biomass
0	2019-01-01 00:00:00	6352	6527	1395	79	1383	1896	1744	0	30
1	2019-01-01 01:00:00	6116	5701	1393	96	1112	1429	1641	0	30
2	2019-01-01 02:00:00	5873	5676	1393	142	1030	1465	1616	0	30
3	2019-01-01 03:00:00	5682	5603	1397	191	972	1455	1558	0	30
4	2019-01-01 04:00:00	5557	5454	1393	159	960	1454	1458	0	30
...	...	...	...	...	...	...	...	...	...	...
36767	2023-03-12 19:00:00	6500	6643	1407	111	3084	933	1047	0	60
36768	2023-03-12 20:00:00	6471	6484	1408	58	2983	933	1042	0	60
36769	2023-03-12 21:00:00	6194	6413	1406	39	2938	935	1031	0	62
36770	2023-03-12 22:00:00	5708	6217	1406	8	2823	917	1001	0	61
36771	2023-03-12 23:00:00	5409	5900	1403	0	2492	914	1030	0	60

regression 1st question linera¶

Regression model 2nd question¶

Kyphosis gcv search and split reducing¶

VOTING FOR HR COMMA SEP¶

Random Forest¶

ASSIGNMENT 2 :¶

bankrupcy¶

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa
...	...	...	...	...	...
145	6.7	3.0	5.2	2.3	virginica
146	6.3	2.5	5.0	1.9	virginica
147	6.5	3.0	5.2	2.0	virginica
148	6.2	3.4	5.4	2.3	virginica
149	5.9	3.0	5.1	1.8	virginica

	x.1	x.2	x.3	x.4	x.5	x.6	x.7	x.8	x.9	x.10	...	x.27	x.28	x.29	x.30	x.31	x.32	x.33	x.34	x.35	x.36
0	92	115	120	94	84	102	106	79	84	102	...	134	104	88	121	128	100	84	107	113	87
1	84	102	106	79	84	102	102	83	80	102	...	128	100	84	107	113	87	84	99	104	79
2	84	102	102	83	80	102	102	79	84	94	...	113	87	84	99	104	79	84	99	104	79
3	80	102	102	79	84	94	102	79	80	94	...	104	79	84	99	104	79	84	103	104	79
4	84	94	102	79	80	94	98	76	80	102	...	104	79	84	103	104	79	79	107	109	87
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6430	60	83	96	85	64	87	100	88	64	83	...	104	92	66	87	108	89	63	83	104	85
6431	64	79	100	85	56	71	96	85	56	68	...	100	85	66	83	100	85	63	83	100	81
6432	56	68	91	81	56	64	91	81	53	64	...	100	81	59	87	96	81	63	83	92	74
6433	56	68	87	74	60	71	91	81	60	64	...	96	74	59	83	92	74	59	83	92	70
6434	60	71	91	81	60	64	104	99	56	64	...	92	74	59	83	92	70	63	79	108	92